Methods and apparatuses for use of simultaneous multiple channels in the dynamic frequency selection band in wireless networks

ABSTRACT

The present invention relates to wireless networks and more specifically to systems and methods for selecting available channels free of radar signals from a plurality of  5  GHz radio frequency channels. In non-limiting embodiments, exemplary systems, methods, and apparatuses are provided that can facilitate reducing false detections and/or network downtime in exemplary mesh networks employing dynamic frequency selection (DFS) channels. In a non-limiting aspect, radar information can be propagated among exemplary mesh nodes, including location information, to facilitate reducing false detections and/or network downtime in exemplary mesh networks. In addition, in further non-limiting aspects, exemplary embodiments can transmit signals to facilitate silencing one or more DFS channels and/or collaborative mesh node identification and/or discrimination of radar signals and false detections, among other non-limiting aspects provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/263,985, entitled METHODS AND APPARATUSES FOR USE OF SIMULTANEOUSMULTIPLE CHANNELS IN THE DYNAMIC FREQUENCY SELECTION BAND IN WIRELESSNETWORKS, and filed on Sep. 13, 2016. This application is acontinuation-in-part of and claims priority to U.S. patent applicationSer. No. 14/920,568, entitled METHOD AND APPARATUS FOR USE OFSIMULTANEOUS MULTIPLE CHANNELS IN THE DYNAMIC FREQUENCY SELECTION BANDIN WIRELESS NETWORKS, and filed on Oct. 22, 2015, which in turn, claimspriority to U.S. Provisional Patent Application No. 62/200,764, entitledMETHOD AND APPARATUS FOR USE OF SIMULTANEOUS MULTIPLE CHANNELS IN THEDYNAMIC FREQUENCY SELECTION BAND IN WIRELESS NETWORKS, and filed on Aug.4, 2015, the disclosures of which are hereby incorporated herein byreference in their entireties. This application is a also acontinuation-in-part of and claims priority to U.S. patent applicationSer. No. 15/171,911, entitled METHOD AND APPARATUS FOR INTEGRATING RADIOAGENT DATA IN NETWORK ORGANIZATION OF DYNAMIC CHANNEL SELECTION INWIRELESS NETWORKS, and filed on Jun. 2, 2016, and U.S. patentapplication Ser. No. 15/214,437, entitled CLOUD DFS SUPER MASTER SYSTEMSAND METHODS, and filed on Jun. 2, 2016, and/or their respectiveprovisional applications, the disclosures of which are herebyincorporated herein by reference in their entireties. In addition, thisapplication is a continuation-in-part of and claims priority to U.S.patent application Ser. No. 15/259,359, entitled METHOD AND APPARATUSFOR PROVIDING DYNAMIC FREQUENCY SELECTION SPECTRUM ACCESS INPEER-TO-PEER WIRELESS NETWORKS, and filed on Sep. 8, 2016, which inturn, claims priority to U.S. Provisional Patent Application No.62/314,042, entitled METHOD AND APPARATUS FOR PROVIDING DYNAMICFREQUENCY SELECTION SPECTRUM ACCESS IN PEER-TO-PEER WIRELESS NETWORKS,and filed on Mar. 28, 2016, the disclosures of which are herebyincorporated herein by reference in their entireties.

BACKGROUND

The present invention relates to wireless networks and more specificallyto systems and methods for selecting available channels free ofoccupying signals from a plurality of radio frequency channels.Embodiments of the present invention provide methods and systems forexploiting licensed and unlicensed bands requiring radar detection anddetection of other occupying signals, such as the Dynamic FrequencySelection (DFS) channels in the Unlicensed National InformationInfrastructure (U-NII) bands, to enable additional bandwidth for 802.11ac/n and LTE in unlicensed spectrum (LTE-U) networks employing awireless agility agent.

Wi-Fi networks are crucial to today's portable modern life. Wi-Fi is thepreferred network in the growing Internet-of-Things (IoT). But, thetechnology behind current Wi-Fi has changed little in the last tenyears. The Wi-Fi network and the associated unlicensed spectrum arecurrently managed in inefficient ways. For example, there is little orno coordination between individual networks and equipment from differentmanufacturers. Such networks generally employ primitive controlalgorithms that assume the network consists of “self-managed islands,” aconcept originally intended for low density and low trafficenvironments. The situation is far worse for home networks, which areassembled in completely chaotic ad hoc ways. Further, with more and moreconnected devices becoming commonplace, the net result is growingcongestion and slowed networks with unreliable connections.

Similarly, LTE-U networks operating in the same or similar unlicensedbands as 802.11ac/n Wi-Fi suffer similar congestion and unreliableconnection issues and will often create congestion problems for existingWi-Fi networks sharing the same channels. Additional bandwidth andbetter and more efficient utilization of spectrum is key to sustainingthe usefulness of wireless networks including the Wi-Fi and LTE-Unetworks in a fast growing connected world.

Devices operating in certain parts of the 5 GHz U-NII-2 band, known asthe DFS channels, require active radar detection. This function isassigned to a device capable of detecting radar known as a DFS master,which is typically an access point or router. The DFS master activelyscans the DFS channels and performs a channel availability check (CAC)and periodic in-service monitoring (ISM) after the channel availabilitycheck. The channel availability check lasts 60 seconds as required bythe FCC Part 15 Subpart E and ETSI 301 893 standards. The DFS mastersignals to the other devices in the network (typically client devices)by transmitting a DFS beacon indicating that the channel is clear ofradar. Although the access point can detect radar, wireless clientstypically cannot. Because of this, wireless clients must first passivelyscan DFS channels to detect whether a beacon is present on thatparticular channel. During a passive scan, the client device switchesthrough channels and listens for a beacon transmitted at regularintervals by the access point on an available channel.

Once a beacon is detected, the client is allowed to actively scan onthat channel. If the DFS master detects radar in that channel, the DFSmaster no longer transmits the beacon, and all client devices upon notsensing the beacon within a prescribed time must vacate the channelimmediately and remain off that channel for 30 minutes. For clientsassociated with the DFS master network, additional information in thebeacons (i.e. the channel switch announcement) can trigger a rapid andcontrolled evacuation of the channel. Normally, a DFS master device isan access point with only one radio and is able to provide DFS masterservices for just a single channel. A significant problem of thisapproach is, in the event of a radar event or a more-commonfalse-detect, the single channel must be vacated and the ability to useDFS channels is lost. This disclosure recognizes and addresses, in atleast certain embodiments, the problems with current devices fordetecting occupying signals including current DFS devices.

SUMMARY

The present invention relates to wireless networks and more specificallyto systems and methods for selecting available channels free ofoccupying signals from a plurality of radio frequency channels. Thepresent invention employs a wireless agility agent to access additionalbandwidth for wireless networks, such as IEEE 802.11ac/n and LTE-Unetworks. The additional bandwidth is derived from channels that requireavoidance of channels with occupying signals. For example, additionalbandwidth is derived from special compliance channels that require radardetection, such as the DFS channels of the U-NII-2 bands, by employingmulti-channel radar detection and in-service monitoring, and activechannel selection controls.

In an embodiment, the present invention utilizes an agility agentemploying proprietary embedded radio techniques including continuousmulti-carrier spectrum monitoring, an embedded computation elementemploying proprietary real-time spectrum analysis algorithms, andproprietary signaling and control protocols to provide detection andcontinuous real-time monitoring of multiple radar types and patterns,and other signals such as interferers and measures of congestion andtraffic, across simultaneous multiple channels.

The present invention may also utilize a cloud-based computation andcontrol element, which together with the wireless agility agent forms asplit-intelligence architecture. In this architecture, the embeddedsensor information from the agility agent—such as radar detectionchannel availability check and in-service monitoring together withmeasurements of interference, traffic, identification of neighboringdevices, and other spectrum and location information—is communicated toand integrated over time within the cloud intelligence engine. Also theembedded sensor information from the agility agent may be fused withspectrum information from other agility agents distributed in space,filtered, and post-processed. The embedded sensor information from theagility agent may further be merged with other data from other sourcesto provide improvements to fundamental signal measurement and networkreliability problems such as augmented radar sensitivity, reducedfalse-detect rates, and reliable discovery of hidden nodes.

In further non-limiting embodiments, exemplary systems, methods, andapparatuses are provided that can facilitate reducing false detectionsand/or network downtime in exemplary mesh networks employing dynamicfrequency selection (DFS) channels. In a non-limiting aspect, radarinformation can be propagated among exemplary mesh nodes, includinglocation information, to facilitate reducing false detections and/ornetwork downtime in exemplary mesh networks. In addition, in furthernon-limiting aspects, exemplary embodiments can transmit signals tofacilitate silencing one or more DFS channels and/or collaborative meshnode identification and/or discrimination of valid radar signals andfalse detections, among other non-limiting aspects provided.

For instance, exemplary methods can comprise receiving in a mesh networkan indication of a suspected radar event on one or more dynamicfrequency selection (DFS) channel, determining whether the suspectedradar event is a valid radar event, based on the suspected radar event,and propagating, in the mesh network, radar information regarding thesuspected radar event or the valid radar event to another mesh node or acloud intelligence engine associated with the mesh network. In furthernon-limiting aspects, exemplary methods can further comprisetransmitting a Clear To Send (CTS) signal or a hold signal on a DFSchannel based on the receiving an indication of the suspected radarevent to facilitate collaborative identification or discriminationbetween valid radar signals and false detections, and/or receivingadditional radar information from another mesh node or the cloudintelligence engine, determining that the suspected radar event is avalid radar event or an invalid radar event and propagating suchinformation in the mesh network.

As another non-limiting example, exemplary systems can comprise one ormore radar detectors configured to receive an indication of a suspectedradar event on one or more DFS channels in a mesh network, amulti-channel DFS master device configured to determine whether thesuspected radar event is a valid radar event, based at least in part onthe suspected radar event, and one or more communications componentassociated the multi-channel DFS master device configured to propagate,in the mesh network, radar information regarding the suspected radarevent or the valid radar event to another mesh node or a cloudintelligence engine associated with the mesh network. In furthernon-limiting aspects, the multi-channel DFS master device can be furtherconfigured to determine that the suspected radar event is a valid or aninvalid radar event (e.g., a false detection), for example, based inpart on additional radar information, including location information,associated with other mesh nodes or the cloud intelligence engineassociated with the mesh network.

Other embodiments and various examples, scenarios and implementationsare described in more detail below. The following description and thedrawings set forth certain illustrative embodiments of thespecification. These embodiments are indicative, however, of but a fewof the various ways in which the principles of the specification may beemployed. Other advantages and novel features of the embodimentsdescribed will become apparent from the following detailed descriptionof the specification when considered in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The aforementioned objects and advantages of the present invention, aswell as additional objects and advantages thereof, will be more fullyunderstood herein after as a result of a detailed description of apreferred embodiment when taken in conjunction with the followingdrawings in which:

FIG. 1 illustrates portions of the 5 GHz Wi-Fi spectrum includingportions that require active monitoring for radar signals.

FIG. 2 illustrates how such an exemplary autonomous DFS master mayinterface with a conventional host access point, a cloud-basedintelligence engine, and client devices in accordance with the presentinvention.

FIG. 3 illustrates how an exemplary autonomous DFS master in apeer-to-peer network may interface with client devices and the cloudintelligence engine independent of any access point, in accordance withthe present invention.

FIG. 4 illustrates a method of performing a channel availability checkphase and in-service monitoring phase in a DFS scanning operation withan autonomous DFS master to make multiple DFS channels of the 5 GHz bandsimultaneously available for use according to the present inventionusing a time-division multiplexed sequential channel availability checkfollowed by continuous in-service monitoring.

FIG. 5 illustrates a method of performing a channel availability checkphase and in-service monitoring phase in a DFS scanning operation withan autonomous DFS master to make multiple DFS channels of the 5 GHz bandsimultaneously available for use according to the present inventionusing a continuous sequential channel availability check followed bycontinuous in-service monitoring.

FIG. 6A illustrates a method of performing a channel availability checkphase and in-service monitoring phase in a DFS scanning operation withan autonomous DFS master to make multiple DFS channels of the 5 GHz bandsimultaneously available for use according to the present invention.

FIG. 6B illustrates an exemplary beacon transmission duty cycle and anexemplary radar detection duty cycle.

FIG. 7 illustrates an embodiment of the present invention in which theagility agent is connected to a host device and connected to a networkvia the host device.

FIG. 8 illustrates another embodiment of the present invention in whichthe agility agent is connected to a host device and connected to anetwork and a cloud intelligence engine via the host device.

FIG. 9 illustrates another embodiment of the present invention in whichthe agility agent is connected to a host device and connected to anetwork and a cloud intelligence engine via the host device.

FIG. 10 illustrates a method of performing a channel availability checkand in-service monitoring of the present invention.

FIG. 11 illustrates another method of performing a channel availabilitycheck and in-service monitoring of the present invention.

FIG. 12 illustrates another method of performing a channel availabilitycheck and in-service monitoring of the present invention.

FIG. 13 illustrates a system that includes a cloud intelligence engine,agility agent(s), a host access point and data source(s), in accordancewith the present invention.

FIGS. 14A and 14B illustrates the logical interface between the wirelessagility agent, the cloud intelligence engine, and an access point (orsimilarly a small cell LTE-U base station).

FIG. 15 illustrates a method of determining an operating channel for aplurality of multi-channel DFS masters using a cloud intelligenceengine, according to the present invention.

FIG. 16 also illustrates additional methods of determining an operatingchannel for a plurality of multi-channel DFS masters using a cloudintelligence engine, according to the present invention.

FIG. 17 illustrates how multiple agility agents provide geographicallydistributed overlapping views of a radar emitter.

FIG. 18 illustrates in a control loop diagram how the cloud intelligenceengine takes the spectrum data from each agility agent, and afterstoring and filtering the data, combines it with similar data from aplurality of other agility agents and cloud data from other sources.

FIG. 19A illustrates the hidden node problem where an access points orsmall cell base station is hidden from view of other access points orsmall cell base stations by topography, obstruction, distance or channelconditions.

FIG. 19B illustrates the hidden radar problem, where a radar emitter isunseen by an agility agent due to topography or obstruction.

FIG. 19C illustrates the hidden radar problem where a radar emitter isunseen by an agility agent due to distance.

FIG. 20 illustrates an exemplary embodiment of the cloud DFS supermaster system in which the cloud DFS super master is communicativelycoupled to a plurality of sensors that detect radar signals in the DFSband and detect wireless traffic information and is communicativelycoupled to one or more client devices.

FIG. 21 illustrates a standard non-DFS enabled peer-to-peer network.

FIG. 22 illustrates an exemplary DFS enabled peer-to-peer network andsystem of the present invention.

FIG. 23 illustrates an exemplary DFS enabled peer-to-peer network andsystem of the present invention.

FIG. 24 illustrates an exemplary DFS enabled peer-to-peer network andsystem of the present invention.

FIG. 25 illustrates an exemplary DFS enabled peer-to-peer network andsystem of the present invention.

FIG. 26 further illustrates a type of DFS peer-to-peer network that isenabled by the present invention.

FIG. 27 further illustrates a type of DFS peer-to-peer network that isenabled by the present invention.

FIG. 28 illustrates an exemplary method according to the presentinvention for providing DFS spectrum access in peer-to-peer wirelessnetworks.

FIG. 29 illustrates additional optional steps in a method according tothe present invention for providing DFS spectrum access in peer-to-peerwireless networks.

FIG. 30 depicts an exemplary functional block diagram of a mesh network,according to various non-limiting aspects as described herein.

FIG. 31 depicts another exemplary functional block diagram of a meshnetwork, according to further non-limiting aspects as described herein.

FIG. 32 depicts other exemplary functional block diagrams of meshnetworks, according to still further non-limiting aspects as describedherein.

FIG. 33 depicts exemplary methods for reducing false detections and/ornetwork downtime in exemplary mesh networks employing DFS channels,according to various non-limiting aspects.

FIG. 34 depicts further non-limiting aspects of exemplary methods forreducing false detections and/or network downtime in exemplary meshnetworks employing DFS channels.

FIG. 35 depicts other exemplary methods for reducing false detectionsand/or network downtime in exemplary mesh networks employing DFSchannels, according to various non-limiting aspects.

FIG. 36 depicts further non-limiting aspects of exemplary methods forreducing false detections and/or network downtime in exemplary meshnetworks employing DFS channels.

FIG. 37 depicts still further non-limiting aspects of exemplary methodsfor reducing false detections and/or network downtime in exemplary meshnetworks employing DFS channels.

FIG. 38 depicts still other exemplary methods for reducing falsedetections and/or network downtime in exemplary mesh networks employingDFS channels, according to various non-limiting aspects.

FIG. 39 depicts still further non-limiting aspects of exemplary methodsfor reducing false detections and/or network downtime in exemplary meshnetworks employing DFS channels.

FIG. 40 depicts other exemplary methods for reducing false detectionsand/or network downtime, based on propagating radar and locationinformation, in exemplary mesh networks employing DFS channels,according to various non-limiting aspects.

FIG. 41 depicts still other exemplary methods for reducing falsedetections and/or network downtime, based on employing a control,management, and/or data frame, in exemplary mesh networks employing DFSchannels, according to various non-limiting aspects.

FIG. 42 depicts still further exemplary methods for reducing falsedetections and/or network downtime, based on employing a hold signaland/or a resume signal, in exemplary mesh networks employing DFSchannels, according to various non-limiting aspects.

FIG. 43 depicts further exemplary methods for reducing false detectionsand/or network downtime in exemplary mesh networks employing DFSchannels, according to various non-limiting aspects.

FIG. 44 depicts a functional block diagram illustrating examplenon-limiting devices or systems suitable for use with aspects of thedisclosed subject matter.

FIG. 45 depicts an example non-limiting device or system suitable forperforming various aspects of the disclosed subject matter.

FIG. 46 is a block diagram representing example non-limiting networkedenvironments in which various embodiments described herein can beimplemented.

FIG. 47 is a block diagram representing an example non-limitingcomputing system or operating environment in which one or more aspectsof various embodiments described herein can be implemented.

FIG. 48 illustrates a schematic diagram of an example mobile device(e.g., a mobile handset, peer-peer device, mesh node, etc.) that canfacilitate various non-limiting aspects of the disclosed subject matterin accordance with the embodiments described herein.

DETAILED DESCRIPTION

The present invention relates to wireless networks and more specificallyto systems and methods for selecting available channels free ofoccupying signals from a plurality of radio frequency channels. As usedherein, a channel “free” of occupying signals may include a channel withoccupying signals that are lower than a signal threshold includingsignal strength, quantity, or traffic. The present invention employs awireless agility agent to access additional bandwidth for wirelessnetworks, such as IEEE 802.11ac/n and LTE-U networks. The additionalbandwidth is derived from channels that require avoidance of occupyingsignals. For example, additional bandwidth is derived from specialcompliance channels that require radar detection—such as the DFSchannels of the U-NII-2 bands—by employing multi-channel radar detectionand in-service monitoring, and active channel selection controls. TheDFS master actively scans the DFS channels and performs a channelavailability check and periodic in-service monitoring after the channelavailability check.

FIG. 1 illustrates portions of the 5 GHz Wi-Fi spectrum 101. FIG. 1shows the frequencies 102 and channels 103 that make up portions of the5 GHz Wi-Fi spectrum 101. The U-NII band is an FCC regulatory domain for5-GHz wireless devices and is part of the radio frequency spectrum usedby IEEE 802.11ac/n devices and by many wireless ISPs. It operates overfour ranges. The U-NII-1 band 105 covers the 5.15-5.25 GHz range. TheU-NII-2A band 106 covers the 5.25-5.35 GHz range. The U-NII-2A band 106is subject to DFS radar detection and avoidance requirements. TheU-NII-2C band 107 covers the 5.47-5.725 GHz range. The U-NII-2C band 107is also subject to DFS radar detection and avoidance requirements. TheU-NII-3 band 109 covers the 5.725 to 5.850 GHz range. Use of the U-NII-3band 109 is restricted in some jurisdictions like the European Union andJapan.

When used in an 802.11ac/n or LTE-U wireless network, the agility agentof the present invention functions as an autonomous DFS master device.In contrast to conventional DFS master devices, the agility agent is notan access point or router, but rather is a standalone wireless deviceemploying inventive scanning techniques described herein that provideDFS scan capabilities across multiple channels, enabling one or moreaccess point devices and peer-to-peer client devices to exploitsimultaneous multiple DFS channels. The standalone autonomous DFS masterof the present invention may be incorporated into another device such asan access point, LTE-U host, base station, cell, or small cell, media orcontent streamer, speaker, television, mobile phone, mobile router,software access point device, or peer to peer device but does not itselfprovide network access to client devices. In particular, in the event ofa radar event or a false-detect, the enabled access point and clients orwireless device are able to move automatically, predictively and veryquickly to another DFS channel.

FIG. 2 provides a detailed illustration of an exemplary system of thepresent invention. As illustrated in FIG. 2, the agility agent 200, inthe role of an autonomous DFS master device, may control at least oneaccess point, the host access point 218, to dictate channel selectionprimarily by (a) signaling availability of one or more DFS channels bysimultaneous transmission of one or more beacon signals; (b)transmitting a listing of both the authorized available DFS channels,herein referred to as a whitelist, and the prohibited DFS channels inwhich a potential radar signal has been detected, herein referred to asa blacklist, along with control signals and a time-stamp signal, hereinreferred to as a dead-man switch timer via an associated non-DFSchannel; (c) transmitting the same signals as (b) over a wired mediumsuch as Ethernet or serial cable; and (d) receiving control,coordination and authorized and preferred channel selection guidanceinformation from the cloud intelligence engine 235. The agility agent200 sends the time-stamp signal, or dead-man switch timer, withcommunications to ensure that the access points 218, 223 do not use theinformation, including the whitelist, beyond the useful lifetime of theinformation. For example, a whitelist will only be valid for certainperiod of time. The time-stamp signal avoids using noncompliant DFSchannels by ensuring that an access point will not use the whitelistbeyond its useful lifetime. The present invention allows currentlyavailable 5 GHz access points without radar detection—which cannotoperate in the DFS channels—to operate in the DFS channels by providingthe radar detection required by the FCC or other regulatory agencies.

The host access point 218 and any other access point devices 223 undercontrol of the autonomous DFS master 200 typically have the controlagent portion 219, 224 installed within their communication stack. Thecontrol agent 219, 224 is an agent that acts under the direction of theagility agent 200 to receive information and commands from the agilityagent 200. The control agent 219, 224 acts on information from theagility agent 200. For example, the control agent 219, 224 listens forinformation like a whitelist or blacklist from the agility agent. If aradar signal is detected by the agility agent 200, the agility agent 200communicates that to the control agent 219, 224, and the control agent219, 224 acts to evacuate the channel immediately. The control agent canalso take commands from the agility agent 200. For example, the hostaccess point 218 and network access point 223 can offload DFS monitoringto the agility agent 200 as long as they can listen to the agility agent200 and take commands from the agility agent regarding available DFSchannels.

The host access point 218 is connected to a wide area network 233 andincludes an access point control agent 219 to facilitate communicationswith the agility agent 200. The access point control agent 219 includesa security module 220 and agent protocols 221 to facilitatecommunication with the agility agent 200, and swarm communicationprotocols 222 to facilitate communications between agility agents,access points, client devices, and other devices in the network. Theagility agent 200 connects to the cloud intelligence engine 235 via thehost access point 218 and the wide area network 233. The access pointsets up a secure tunnel to communicate with the cloud intelligenceengine 235 through, for example, an encrypted control API in the hostaccess point 218. The agility agent 200 transmits information to thecloud intelligence engine 235 such as whitelists, blacklists, stateinformation, location information, time signals, scan lists (forexample, showing neighboring access points), congestion (for example,number and type of re-try packets), and traffic information. The cloudintelligence engine 235 communicates information to the agility agent200 via the secure communications tunnel such as access point location(including neighboring access points), access point/cluster currentstate and history, statistics (including traffic, congestion, andthroughput), whitelists, blacklists, authentication information,associated client information, and regional and regulatory information.The agility agent 200 uses the information from the cloud intelligenceengine 235 to control the access points and other network devices.

The agility agent 200 may communicate via wired connections or wirelessly with the other network components. In the illustrated example, theagility agent 200 includes a primary radio 215 and a secondary radio216. The primary radio 215 is for DFS and radar detection and istypically a 5 GHz radio. The agility agent 200 may receive radarsignals, traffic information, and/or congestion information through theprimary radio 215. And the agility agent 200 may transmit informationsuch as DFS beacons via the primary radio 215. The second radio 216 is asecondary radio for sending control signals to other devices in thenetwork and is typically a 2.4 GHz radio. The agility agent 200 mayreceive information such as network traffic, congestion, and/or controlsignals with the secondary radio 216. And the agility agent 200 maytransmit information such as control signals with the secondary radio216. The primary radio 215 is connected to a fast channel switchinggenerator 217 that includes a switch and allows the primary radio 215 toswitch rapidly between a radar detector 211 and beacon generator 212.The channel switching generator 217 allows the radar detector 211 toswitch sufficiently fast to appear to be on multiple channels at a time.

In one embodiment, a standalone multi-channel DFS master includes abeacon generator 212 to generate a beacon in each of a plurality of 5GHz radio channels, a radar detector 211 to scan for a radar signal ineach of the plurality of 5 GHz radio channels, a 5 GHz radio transceiver215 to transmit the beacon in each of the plurality of 5 GHz radiochannels and to receive the radar signal in each of the plurality of 5GHz radio channels, and a fast channel switching generator 217 coupledto the radar detector, the beacon generator, and the 5 GHz radiotransceiver. The fast channel switching generator 217 switches the 5 GHzradio to a first channel of the plurality of 5 GHz radio channels andthen causes the beacon generator 212 to generate the beacon in the firstchannel of the plurality of 5 GHz radio channels. Then the fast channelswitching generator 217 causes the radar detector 211 to scan for theradar signal in the first channel of the plurality of 5 GHz radiochannels. The fast channel switching generator 217 then repeats thesesteps for each other channel of the plurality of 5 GHz radio channelsduring a beacon transmission duty cycle and, in some examples, during aradar detection duty cycle. The beacon transmission duty cycle is thetime between successive beacon transmissions on a given channel and theradar detection duty cycle which is the time between successive scans ona given channel. Because the agility agent 200 cycles between beaconingand scanning in each of the plurality of 5 GHz radio channels in thetime window between a first beaconing and scanning in a given channeland a subsequent beaconing and scanning the same channel, it can provideeffectively simultaneous beaconing and scanning for multiple channels.

The agility agent 200 also may contain a Bluetooth radio 214 and an802.15.4 radio 213 for communicating with other devices in the network.The agility agent 200 may include various radio protocols 208 tofacilitate communication via the included radio devices.

The agility agent 200 may also include a location module 209 togeolocate or otherwise determine the location of the agility agent 200.As shown in FIG. 2, the agility agent 200 may include a scan andsignaling module 210. The agility agent 200 includes embedded memory202, including for example flash storage 201, and an embedded processor203. The cloud agent 204 in the agility agent 200 facilitatesaggregation of information from the cloud agent 204 through the cloudand includes swarm communication protocols 205 to facilitatecommunications between agility agents, access points, client devices,and other devices in the network. The cloud agent 204 also includes asecurity module 206 to protect and secure the agility agent's 200 cloudcommunications as well as agent protocols 207 to facilitatecommunication with the access point control agents 219, 224.

As shown in FIG. 2, the agility agent 200 may control other accesspoints, for example networked access point 223, in addition to the hostaccess point 218. The agility agent 200 may communicate with the otheraccess points 223 via a wired or wireless connection 236, 237. The otheraccess points 223 include an access point control agent 224 tofacilitate communication with the agility agent 200 and other accesspoints. The access point control agent 224 includes a security module225, agent protocols 226 and swarm communication protocols 227 tofacilitate communications with other agents (including other accesspoints and client devices) on the network.

The cloud intelligence engine 235 includes a database 248 and memory 249for storing information from the agility agent 200, other agility agents(not shown) connected to the intelligence engine 235, and external datasources (not shown). The database 248 and memory 249 allow the cloudintelligence engine 235 to store information over months and yearsreceived from agility agents and external data sources.

The cloud intelligence engine 235 also includes processors 250 toperform the cloud intelligence operations described herein. The roamingand guest agents manager 238 in the cloud intelligence engine 235provides optimized connection information for devices connected toagility agents that are roaming from one access point to other or fromone access point to another network. The roaming and guest agentsmanager 238 also manages guest connections to networks for agilityagents connected to the cloud intelligence engine 235. The external datafusion engine 239 provides for integration and fusion of informationfrom agility agents with information from external data sources forexample GIS information, other geographical information, FCC informationregarding the location of radar transmitters, FCC blacklist information,NOAA databases, DOD information regarding radar transmitters, and DODrequests to avoid transmission in DFS channels for a given location. Thecloud intelligence engine 235 further includes an authenticationinterface 240 for authentication of received communications and forauthenticating devices and users. The radar detection compute engine 241aggregates radar information from agility agents and external datasources and computes the location of radar transmitters from those datato, among other things, facilitate identification of false positiveradar detections or hidden nodes and hidden radar. The radar detectioncompute engine 241 may also guide or steer multiple agility agents todynamically adapt detection parameters and/or methods to further improvedetection sensitivity. The location compute and agents manager 242determines the location the agility agent 200 and other connecteddevices through Wi-Fi lookup in a Wi-Fi location database, queryingpassing devices, scan lists from agility agents, or geometric inference.

The spectrum analysis and data fusion engine 243 and the networkoptimization self-organization engine 244 facilitate dynamic spectrumoptimization with information from the agility agents and external datasources. Each of the agility agents connected to the cloud intelligenceengine 235 have scanned and analyzed the local spectrum and communicatedthat information to the cloud intelligence engine 235. The cloudintelligence engine 235 also knows the location of each agility agentand the access points proximate to the agility agents that do not have acontrolling agent as well as the channel on which each of those devicesis operating. With this information, the spectrum analysis and datafusion engine 243 and the network optimization self-organization engine244 can optimize the local spectrum by telling agility agents to avoidchannels subject to interference. The swarm communications manager 245manages communications between agility agents, access points, clientdevices, and other devices in the network. The cloud intelligence engineincludes a security manager 246. The control agents manager 247 managesall connected control agents.

Independent of a host access point 218, the agility agent 200, in therole of an autonomous DFS master device, may also provide the channelindication and channel selection control to one or more peer-to-peerclient devices 231, 232 within the coverage area by (a) signalingavailability of one or more DFS channels by simultaneous transmission ofone or more beacon signals; (b) transmitting a listing of both theauthorized available DFS channels, herein referred to as a whitelist andthe prohibited DFS channels in which a potential radar signal has beendetected, herein referred to as a blacklist along with control signalsand a time-stamp signal, herein referred to as a dead-man switch timervia an associated non-DFS channel; and (c) receiving control,coordination and authorized and preferred channel selection guidanceinformation from the cloud intelligence engine 235. The agility agent200 sends the time-stamp signal, or dead-man switch timer, withcommunications to ensure that the devices do not use the information,including the whitelist, beyond the useful lifetime of the information.For example, a whitelist will only be valid for certain period of time.The time-stamp signal avoids using noncompliant DFS channels by ensuringthat a device will not use the whitelist beyond its useful lifetime.

Such peer-to-peer devices may have a user control interface 228. Theuser control interface 228 includes a user interface 229 to allow theclient devices 231, 232 to interact with the agility agent 200 via thecloud intelligence engine 235. For example, the user interface 229allows the user to modify network settings via the agility agent 200including granting and revoking network access. The user controlinterface 228 also includes a security element 230 to ensure thatcommunications between the client devices 231, 232 and the agility agent200 are secure. The client devices 231, 232 are connected to a wide areanetwork 234 via a cellular network for example. Peer-to-peer wirelessnetworks are used for direct communication between devices without anaccess point. For example, video cameras may connect directly to acomputer to download video or images files using a peer-to-peer network.Also, device connections to external monitors and device connections todrones currently use peer-to-peer networks. Because there is no accesspoint in a peer-to-peer network, traditional peer-to-peer networkscannot use the DFS channels because there is no access point to controlthe DFS channel selection and tell the devices what DFS channels to use.The present invention overcomes this limitation.

FIG. 3 illustrates how the agility agent 200 acting as an autonomous DFSmaster in a peer-to-peer network 300 (a local area network for example)would interface to client devices 231, 232, 331 and the cloudintelligence engine 235 independent of any access point, in accordancewith the present invention. As shown in FIG. 3, the cloud intelligenceengine 235 may be connected to a plurality of network-connected agilityagents 200, 310. The agility agent 200 in the peer-to-peer network 300may connect to the cloud intelligence engine 235 through one of thenetwork-connected client devices 231, 331 by, for example, piggy-backinga message to the cloud intelligence engine 235 on a message send to theclient devices 231, 331 or otherwise coopting the client devices' 231,331 connection to the wide area network 234. In the peer-to-peer network300, the agility agent 200 sends over-the-air control signals 320 to theclient devices 231, 232, 331 including indications of channels free ofoccupying signals such as DFS channels free of radar signals.Alternatively, the agility agent communicates with just one clientdevice 331 which then acts as the group owner to initiate and controlthe peer-to-peer communications with other client devices 231, 232. Theclient devices 231, 232, 331 have peer-to-peer links 321 through whichthey communicate with each other.

The agility agent may operate in multiple modes executing a number ofDFS scan methods employing different algorithms. Two of these methodsare illustrated in FIG. 4 and FIG. 5.

FIG. 4 illustrates a first DFS scan method 400 for a multi-channel DFSmaster of the present invention. This method uses a time divisionsequential CAC 401 followed by continuous ISM 402. The method begins atstep 403 with the multi-channel DFS master at startup or after a reset.At step 404 the embedded radio is set to receive (Rx) and is tuned tothe first DFS channel (C=1). In one example, the first channel ischannel 52. Next, because this is the first scan after startup or resetand the DFS master does not have information about channels free ofradar, the DFS master performs a continuous CAC 405 scan for a period of60 seconds (compliant with the FCC Part 15 Subpart E and ETSI 301 893requirements). At step 406 the DFS master determines if a radar patternis present in the current channel. If radar pattern is detected 407,then the DFS master marks this channel in the blacklist. The DFS mastermay also send additional information about the detected radar includingthe signal strength, radar pattern, type of radar, and a time stamp forthe detection.

At the first scan after startup or reset, if a radar pattern is detectedin the first channel scanned, the DFS master may repeat the above stepsuntil a channel free of radar signals is found. Alternatively, after astartup or reset, the DFS master may be provided a whitelist indicatingone or more channels that have been determined to be free of radarsignals. For example, the DFS master may receive a message that channel52 is free of radar signals from the cloud intelligence engine 235 alongwith information fused from other sources.

If at step 406 the DFS master does not detect a radar pattern 410, theDFS master marks this channel in the whitelist and switches the embeddedradio to transmit (Tx) (not shown in FIG. 4) at this channel. The DFSmaster may include additional information in the whitelist including atime stamp. The DFS master then transmits (not shown in FIG. 4) a DFSmaster beacon signal for minimum required period of n (which is theperiod of the beacon transmission defined by IEEE 802.11 requirements,usually very short on the order of a few microseconds). A common SSIDmay be used for all beacons of our system.

For the next channel scan after the DFS master finds a channel free ofradar, the DFS master sets the radio to receive and tunes the radio tothe next DFS channel 404 (for example channel 60). The DFS master thenperforms a non-continuous CAC radar detection scan 405 for period of X,which is the maximum period between beacons allowable for a clientdevice to remain associated with a network (P_(M)) less a period of nrequired for a quick radar scan and the transmission of the beaconitself (X=P_(M)-n) 408. At 411, the DFS master saves the state ofcurrent non-continuous channel state (S_(C)) from the non-continuous CACscan so that the DFS master can later resume the current non-continuouschannel scan at the point where the DFS master left off. Then, at step412, the DFS master switches the radio to transmit and tunes to thefirst DFS channel (in this example it was CH 52), performs quick receiveradar scan 413 (for a period of D called the dwell time) to detect radar414. If a radar pattern is detected, the DFS master marks the channel tothe blacklist 418. When marking the channel to the blacklist, the DFSmaster may also include additional information about the detected radarpattern including signal strength, type of radar, and a time stamp forthe detection. If no radar pattern is detected, the DFS master transmitsagain 415 the DFS master beacon for the first channel (channel 52 in theexample). Next, the DFS master determines if the current channel (C_(B))is the last channel in the whitelist (W_(L)) 416. In the currentexample, the current channel, channel 52, is the only channel in thewhitelist at this point. Then, the DFS master restores 417 the channelto the saved state from step 411 and switches the radio back to receivemode and tunes the radio back to the current non-continuous CAC DFSchannel (channel 60 in the example) 404. The DFS master then resumes thenon-continuous CAC radar scan 405 for period of X, again accommodatingthe period of n required for the quick scan and transmission of thebeacon. This is repeated until 60 seconds of non-continuous CAC scanningis accumulated 409—in which case the channel is marked in the whitelist410—or until a radar pattern is detected—in which case this channel ismarked in the blacklist 407.

Next, the DFS master repeats the procedure in the preceding paragraphfor the next DFS channel (for example channel 100). The DFS masterperiodically switches 412 to previous whitelisted DFS channels to do aquick scan 413 (for a period of D called the dwell time), and if noradar pattern detected, transmits a beacon 415 for period of n in eachof the previously CAC scanned and whitelisted DFS channels. Then the DFSmaster returns 404 to resume the non-continuous CAC scan 405 of thecurrent CAC channel (in this case CH 100). The period X available fornon-continuous CAC scanning before switching to transmit andsequentially beaconing the previously whitelisted CAC scanned channelsis reduced by n for each of the previously whitelisted CAC scannedchannels, roughly X=P_(M)-n*(W_(L)) where W_(L) is the number ofpreviously whitelisted CAC scanned channels. This is repeated until 60seconds of non-continuous CAC scanning is accumulated for the currentchannel 409. If no radar pattern is detected the channel is marked inthe whitelist 410. If a radar pattern is detected, the channel is markedin the blacklist 407 and the radio can immediately switch to the nextDFS channel to be CAC scanned.

The steps in the preceding paragraph are repeated for each new DFSchannel until all desired channels in the DFS band have been CACscanned. In FIG. 4, step 419 checks to see if the current channel C isthe last channel to be CAC scanned R. If the last channel to be CACscanned R has been reached, the DFS master signals 420 that the CACphase 401 is complete and begins the ISM phase 402. The whitelist andblacklist information may be communicated to the cloud intelligenceengine where it is integrated over time and fused with similarinformation from other agility agents.

During the ISM phase, the DFS master does not scan the channels in theblacklist 421. The DFS master switches 422 to the first channel in thewhitelist and transmits 423 a DFS beacon on that channel. Then the DFSmaster scans 424 the first channel in the whitelist for a period ofD_(ISM) (the ISM dwell time) 425, which may be roughly P_(M) (themaximum period between beacons allowable for a client device to remainassociated with a network) minus n times the number of whitelistedchannels, divided by the number of whitelisted channels(D_(ISM)=(P_(M)-n*W_(L))/n). Then the DFS master transmits 423 a beaconand scans 424 each of the channels in the whitelist for the dwell timeand then repeats starting at the first channel in the whitelist 422 in around robin fashion for each respective channel. If a radar pattern isdetected 426, the DFS master beacon for the respective channel isstopped 427, and the channel is marked in the blacklist 428 and removedfrom the whitelist (and no longer ISM scanned). The DFS master sendsalert messages 429, along with the new whitelist and blacklist to thecloud intelligence engine. Alert messages may also be sent to otheraccess points and/or client devices in the network.

FIG. 5 illustrates a second DFS scan method 500 for a multi-channel DFSmaster of the present invention. This method uses a continuoussequential CAC 501 followed by continuous ISM 502. The method begins atstep 503 with the multi-channel DFS master at startup or after a reset.At step 504 the embedded radio is set to receive (Rx) and is tuned tothe first DFS channel (C=1). In this example, the first channel ischannel 52. The DFS master performs a continuous CAC scan 505 for aperiod of 60 seconds 507 (compliant with the FCC Part 15 Subpart E andETSI 301 893 requirements). If radar pattern is detected at step 506then the DFS master marks this channel in the blacklist 508.

If the DFS master does not detect radar patterns, it marks this channelin the whitelist 509. The DFS master determines if the current channel Cis the last channel to be CAC scanned R at step 510. If not, then theDFS master tunes the receiver to the next DFS channel (for examplechannel 60) 504. Then the DFS master performs a continuous scan 505 forfull period of 60 seconds 507. If a radar pattern is detected, the DFSmaster marks the channel in the blacklist 508 and the radio canimmediately switch to the next DFS channel 504 and repeat the stepsafter step 504.

If no radar pattern is detected 509, the DFS master marks the channel inthe whitelist 509 and then tunes the receiver next DFS channel 504 andrepeats the subsequent steps until all DFS channels for which a CAC scanis desired. Unlike the method depicted in FIG. 4, no beacon istransmitted between CAC scans of sequential DFS channels during the CACscan phase.

The ISM phase 502 in FIG. 5 is identical to that in FIG. 4 describedabove.

FIG. 6A illustrates how multiple channels in the DFS channels of the 5GHz band are made simultaneously available by use of the invention. FIG.6A illustrates the process of FIG. 5 wherein the autonomous DFS Masterperforms the DFS scanning CAC phase 600 across multiple channels andupon completion of CAC phase, the autonomous DFS Master performs the ISMphase 601. During the ISM phase the DFS master transmits multiplebeacons to indicate the availability of multiple DFS channels to nearbyhost and non-host (ordinary) access points and client devices, inaccordance with the present invention.

FIG. 6A shows the frequencies 602 and channels 603 that make up portionsof the DFS 5 GHz Wi-Fi spectrum. U-NII-2A 606 covers the 5.25-5.35 GHzrange. U-NII-2C 607 covers the 5.47-5.725 GHz range. The first channelto undergo CAC scanning is shown at element 607. The subsequent CACscans of other channels are shown at elements 608. And the final CACscan before the ISM phase 601 is shown at element 609.

In the ISM phase 601, the DFS master switches to the first channel inthe whitelist. In the example in FIG. 6A, each channel 603 for which aCAC scan was performed was free of radar signals during the CAC scan andwas added to the whitelist. Then the DFS master transmits 610 a DFSbeacon on that channel. Then the DFS master scans 620 the first channelin the whitelist for the dwell time. Then the DFS master transmits 611 abeacon and scans 621 each of the other channels in the whitelist for thedwell time and then repeats starting 610 at the first channel in thewhitelist in a round robin fashion for each respective channel. If aradar pattern is detected, the DFS master beacon for the respectivechannel is stopped, and the channel is marked in the blacklist andremoved from the whitelist (and no longer ISM scanned).

FIG. 6A also shows an exemplary waveform 630 of the multiple beacontransmissions from the DFS master to indicate the availability of themultiple DFS channels to nearby host and non-host (ordinary) accesspoints and client devices.

FIG. 6B illustrates a beacon transmission duty cycle 650 and a radardetection duty cycle 651. In this example, channel A is the firstchannel in a channel whitelist. In FIG. 6B, a beacon transmission inchannel A 660 is followed by a quick scan of channel A 670. Next abeacon transmission in the second channel, channel B, 661 is followed bya quick scan of channel B 671. This sequence is repeated for channels C662, 672; D 663, 673; E 664, 674; F 665, 675; G 666, 676, and H 667,677. After the quick scan of channel H 677, the DFS master switches backto channel A and performs a second beacon transmission in channel A 660followed by a second quick scan of channel A 670. The time betweenstarting the first beacon transmission in channel A and starting thesecond beacon transmission in channel A is a beacon transmission dutycycle. The time between starting the first quick scan in channel A andstarting the second quick scan in channel A is a radar detection dutycycle. In order to maintain connection with devices on a network, thebeacon transmission duty cycle should be less than or equal to themaximum period between the beacons allowable for a client device toremain associated with the network.

One embodiment of the present invention provides a standalonemulti-channel DFS master that includes a beacon generator 212 togenerate a beacon in each of a plurality of 5 GHz radio channels, aradar detector 211 to scan for a radar signal in each of the pluralityof 5 GHz radio channels, a 5 GHz radio transceiver 215 to transmit thebeacon in each of the plurality of 5 GHz radio channels and to receivethe radar signal in each of the plurality of 5 GHz radio channels, and afast channel switching generator 217 and embedded processor 203 coupledto the radar detector, the beacon generator, and the 5 GHz radiotransceiver. The fast channel switching generator 217 and embeddedprocessor 203 switch the 5 GHz radio transceiver 215 to a first channelof the plurality of 5 GHz radio channels and cause the beacon generator212 to generate the beacon in the first channel of the plurality of 5GHz radio channels. The fast channel switching generator 217 andembedded processor 203 also cause the radar detector 211 to scan for theradar signal in the first channel of the plurality of 5 GHz radiochannels. The fast channel switching generator 217 and embeddedprocessor 203 then repeat these steps for each of the other channels ofthe plurality of 5 GHz radio channels. The fast channel switchinggenerator 217 and embedded processor 203 perform all of the steps forall of the plurality of 5 GHz radio channels during a beacontransmission duty cycle which is a time between successive beacontransmissions on a specific channel and, in some embodiments, a radardetection duty cycle which is a time between successive scans on thespecific channel.

In the embodiment illustrated in FIG. 7, the present invention includessystems and methods for selecting available channels free of occupyingsignals from a plurality of radio frequency channels. The systemincludes an agility agent 700 functioning as an autonomous frequencyselection master that has both an embedded radio receiver 702 to detectthe occupying signals in each of the plurality of radio frequencychannels and an embedded radio transmitter 703 to transmit an indicationof the available channels and an indication of unavailable channels notfree of the occupying signals. The agility agent 700 is programmed toconnect to a host device 701 and control a selection of an operatingchannel selection of the host device by transmitting the indication ofthe available channels and the indication of the unavailable channels tothe host device. The host device 701 communicates wirelessly with clientdevices 720 and acts as a gateway for client devices to a network 710such as the Internet, other wide area network, or local area network.The host device 701, under the control of the agility agent 700, tellsthe client devices 720 which channel or channels to use for wirelesscommunication. Additionally, the agility agent 700 may be programmed totransmit the indication of the available channels and the indication ofthe unavailable channels directly to client devices 720.

The agility agent 700 may operate in the 5 GHz band and the plurality ofradio frequency channels may be in the 5 GHz band and the occupyingsignals are radar signals. The host device 701 may be a Wi-Fi accesspoint or an LTE-U host device.

Further, the agility agent 700 may also be programmed to transmit theindication of the available channels by simultaneously transmittingmultiple beacon signals. And the agility agent 700 may be programmed totransmit the indication of the available channels by transmitting achannel whitelist of the available channels and to transmit theindication of the unavailable channels by transmitting a channelblacklist of the unavailable channels. In addition to saving the channelin the channel blacklist, the agility agent 700 may also be programmedto determine and save in the channel blacklist information about thedetected occupying signals including signal strength, traffic, and typeof the occupying signals.

As shown in FIG. 8, in some embodiments, the agility agent 700 isconnected to a cloud-based intelligence engine 855. The agility agent700 may connect to the cloud intelligence engine 855 directly or throughthe host device 701 and network 710. The cloud intelligence engine 855integrates time distributed information from the agility agent 700 andcombines information from a plurality of other agility agents 850distributed in space and connected to the cloud intelligence engine 855.The agility agent 700 is programmed to receive control and coordinationsignals and authorized and preferred channel selection guidanceinformation from the cloud intelligence engine 755.

In another embodiment shown in FIG. 9, the present invention includes asystem and method for selecting available channels free of occupyingsignals from a plurality of radio frequency channels in which an agilityagent 700 functioning as an autonomous frequency selection masterincludes an embedded radio receiver 702 to detect the occupying signalsin each of the plurality of radio frequency channels and an embeddedradio transmitter 703 to indicate the available channels and unavailablechannels not free of the occupying signals. The agility agent 700contains a channel whitelist 910 of one or more channels scanned anddetermined not to contain an occupying signal. The agility agent 700 mayreceive the whitelist 910 from another device including a cloudintelligence engine 855. Or the agility agent 700 may have previouslyderived the whitelist 910 through a continuous CAC for one or morechannels. In this embodiment, the agility agent 700 is programmed tocause the embedded radio receiver 702 to scan each of the plurality ofradio frequency channels non-continuously interspersed with periodicswitching to the channels in the channel whitelist 910 to perform aquick occupying signal scan in each channel in the channel whitelist910. The agility agent 700 is further programmed to cause the embeddedradio transmitter 703 to transmit a first beacon transmission in eachchannel in the channel whitelist 910 during the quick occupying signalscan and to track in the channel whitelist 910 the channels scanned anddetermined not to contain the occupying signal during the non-continuousscan and the quick occupying signal scan. The agility agent 700 is alsoprogrammed to track in a channel blacklist 915 the channels scanned anddetermined to contain the occupying signal during the non-continuousscan and the quick occupying signal scan and then to perform in-servicemonitoring for the occupying signal, including transmitting a secondbeacon for each of the channels in the channel whitelist 910,continuously and sequentially.

FIG. 10 illustrates an exemplary method 1000 according to the presentinvention for selecting an operating channel from a plurality of radiofrequency channels in an agility agent functioning as an autonomousfrequency selection master. The method includes receiving a channelwhitelist of one or more channels scanned and determined not to containan occupying signal 1010. Next, the agility agent performs a channelavailability check 1005 for the plurality of radio frequency channels ina time-division manner. The time-division channel availability checkincludes scanning 1010 with an embedded radio receiver in the agilityagent each of the plurality of radio frequency channels non-continuouslyinterspersed with periodic switching to the channels in the channelwhitelist to perform a quick occupying signal scan and transmitting 1020a first beacon with an embedded radio transmitter in the agility agentin each channel in the channel whitelist during the quick occupyingsignal scan. The agility agent also tracks 1030 in the channel whitelistthe channels scanned in step 1010 and determined not to contain theoccupying signal and tracks 1040 in a channel blacklist the channelsscanned in step 1010 and determined to contain the occupying signal.Finally, the agility agent performs in-service monitoring for theoccupying signal and a second beaconing transmission for each of thechannels in the channel whitelist continuously and sequentially 1050.

FIG. 11 illustrates another exemplary method 1100 for selecting anoperating channel from a plurality of radio frequency channels in anagility agent functioning as an autonomous frequency selection master.The method 1100 includes performing a channel availability check foreach of the plurality of radio frequency channels by scanning 1101 withan embedded radio receiver in the agility agent each of the plurality ofradio frequency channels continuously for a scan period. The agilityagent then tracks 1110 in a channel whitelist the channels scanned anddetermined not to contain an occupying signal and tracks 1120 in achannel blacklist the channels scanned and determined to contain theoccupying signal. Then the agility agent performs in-service monitoringfor the occupying signal and transmits a beacon with an embedded radiotransmitter in the agility agent for each of the channels in the channelwhitelist continuously and sequentially 1130.

FIG. 12 illustrates a further exemplary method 1200 for selecting anoperating channel from a plurality of radio frequency channels in anagility agent functioning as an autonomous frequency selection master.The method 1200 includes performing a channel availability check 1210for each of the plurality of radio frequency channels and performingin-service monitoring and beaconing 1250 for each of the plurality ofradio frequency channels. The channel availability check 1210 includestuning an embedded radio receiver in the autonomous frequency selectionmaster device to one of the plurality of radio frequency channels andinitiating a continuous channel availability scan in the one of theplurality of radio frequency channels with the embedded radio receiver1211. Next, the channel availability check 1210 includes determining ifan occupying signal is present in the one of the plurality of radiofrequency channels during the continuous channel availability scan 1212.If the occupying signal is present in the one of the plurality of radiofrequency channels during the continuous channel availability scan, thechannel availability check 1210 includes adding the one of the pluralityof radio frequency channels to a channel blacklist and ending thecontinuous channel availability scan 1213. If the occupying signal isnot present in the one of the plurality of radio frequency channelsduring the continuous channel availability scan during a first scanperiod, the channel availability check 1210 includes adding the one ofthe plurality of radio frequency channels to a channel whitelist andending the continuous channel availability scan 1214. Next, the channelavailability check 1210 includes repeating steps 1211 and 1212 andeither 1213 or 1214 for each of the plurality of radio frequencychannels.

The in-service monitoring and beaconing 1250 for each of the pluralityof radio frequency channels includes determining if the one of theplurality of radio frequency channels is in the channel whitelist and ifso, tuning the embedded radio receiver in the autonomous frequencyselection master device to the one of the plurality of radio frequencychannels and transmitting a beacon in the one of the plurality of radiofrequency channels with an embedded radio transmitter in the autonomousfrequency selection master device 1251. Next, the in-service monitoringand beaconing 1250 includes initiating a discrete channel availabilityscan (a quick scan as described previously) in the one of the pluralityof radio frequency channels with the embedded radio receiver 1252. Next,the in-service monitoring and beaconing 1250 includes determining if theoccupying signal is present in the one of the plurality of radiofrequency channels during the discrete channel availability scan 1253.If the occupying signal is present, the in-service monitoring andbeaconing 1250 includes stopping transmission of the beacon, removingthe one of the plurality of radio frequency channels from the channelwhitelist, adding the one of the plurality of radio frequency channelsto the channel blacklist, and ending the discrete channel availabilityscan 1254. If the occupying signal is not present in the one of theplurality of radio frequency channels during the discrete channelavailability scan for a second scan period, the in-service monitoringand beaconing 1250 includes ending the discrete channel availabilityscan 1255. Thereafter, the in-service monitoring and beaconing 1250includes repeating steps 1251, 1252, and 1253 as well as either 1254 or1255 for each of the plurality of radio frequency channels.

FIG. 13 illustrates a system that includes the cloud intelligence engine235, the agility agent 200 and the host access point 218. The agilityagent 200 may be directed by the cloud intelligence engine 235 (e.g., acloud-based data fusion and computation element) to enable adaptivecontrol of dynamic channel selection for the host access point 218and/or other functions (e.g., dynamic configuration of radio parameters,etc.) associated with the host access point 218. As disclosed herein, inan aspect, the agility agent 200 includes the cloud agent 204. Forexample, the cloud agent 204 may enable the agility agent 200 tocommunicate with the host access point 218. The cloud agent 204 mayadditionally or alternatively communicate with one or more other devices(not shown) such as, for example, a base station (e.g., a small cellbase station), a DFS slave device, a peer-to-peer group owner device, amobile hotspot device, a radio access node device (e.g., an LTE-smallcell device), a software access point device and/or another device. Inan implementation, the cloud agent 204 includes cloud control 1302. Thecloud control 1302 may further enable the agility agent 200 tocommunicate with the cloud intelligence engine 235. Furthermore, thecloud control 1302 may facilitate dynamic selection of radio channelsand/or other radio frequency parameters for the host access point 218.For example, the agility agent 200 may analyze a plurality of 5 GHzradio channels (e.g., a plurality of 5 GHz radio channels associatedwith the 5 GHz Wi-Fi spectrum 161) for the host access point 218.Additionally or alternatively, the agility agent 200 may analyze aplurality of 5 GHz radio channels (e.g., a plurality of 5 GHz radiochannels associated with the 5 GHz Wi-Fi spectrum 161) for the DFS slavedevice, the peer-to-peer group owner device, the mobile hotspot device,the radio access node device (e.g., the LTE-small cell device), thesoftware access point device and/or another device. In an aspect, theagility agent 200 may actively scan the plurality of 5 GHz radiochannels (e.g., the plurality of 5 GHz radio channels associated withthe 5 GHz Wi-Fi spectrum 161) during a CAC phase and/or during an ISMphase.

Then, the agility agent 200 may generate spectral information based onthe analysis of the plurality of 5 GHz radio channels (e.g., theplurality of 5 GHz radio channels for the host access point 218, the DFSslave device, the peer-to-peer group owner device, the mobile hotspotdevice, the radio access node device, the software access point deviceand/or another device). For example, the agility agent 200 may provideinformation (e.g., spectral information) to the cloud intelligenceengine 235 that indicates a set of channels from the plurality of 5 GHzradio channels which are clear of radar and are thus available to use bynearby devices (e.g., the host access point 218). The spectralinformation may include information such as, for example, a whitelist(e.g., a whitelist of each of the plurality of 5 GHz radio channels thatdoes not contain a radar signal), a blacklist (e.g., a blacklist of eachof the plurality of 5 GHz radio channels that contains a radar signal),scan information associated with a scan for a radar signal in theplurality of 5 GHz radio channels, state information, locationinformation associated with the agility agent 200 and/or the host accesspoint 218, time signals, scan lists (e.g., scan lists showingneighboring access points, etc.), congestion information (e.g., numberof re-try packets, type of re-try packets, etc.), traffic information,other channel condition information, and/or other spectral information.The cloud control 1302 may transmit the spectral information to thecloud intelligence engine 235. In an aspect, the agility agent 200 maytransmit the spectral information to the cloud intelligence engine 235via a wide area network. Additionally or alternatively, the agilityagent 200 may transmit the spectral information to the cloudintelligence engine 235 via a set of DFS slave devices in communicationwith the agility agent 200 (e.g., via a backhaul of DFS slave devices incommunication with the agility agent 200). In another aspect, theagility agent 200 may be in communication with the host access point 218via a local area network (e.g., a wireless local area network).Additionally or alternatively, the agility agent 200 may be incommunication with the host access point 218 via a wide area network(e.g., a wireless wide area network), an ad hoc network (e.g., an IBSSnetwork), a peer-to-peer network (e.g., an IBSS peer-to-peer network), ashort range wireless network (e.g., a Bluetooth network), anotherwireless network and/or another wired network.

The cloud intelligence engine 235 may integrate the spectral informationwith other spectral information (e.g., other spectral informationassociated with the agility agent(s) 251) to generate integratedspectral information. For example, the cloud intelligence engine 235 mayreceive the other spectral information from the agility agent(s) 251.The other spectral information may be generated by the agility agents(s)251 via an analysis of the plurality of 5 GHz radio channels (e.g., ananalysis similarly performed by the agility agent 200). In an aspect,the cloud intelligence engine 235 may include a cloud-based data fusionand computation element for intelligent adaptive network organization,optimization, planning, configuration, management and/or coordinationbased on the spectral information and the other spectral information.The cloud intelligence engine 235 may geo-tag, filter and/or process theintegrated spectral information. In an implementation, the cloudintelligence engine 235 may combine the integrated spectral informationwith regulation information associated with the data source(s) 252. Forexample, the regulation information (e.g., non-spectral information)associated with the data source(s) 252 may include information such as,but not limited to, geographical information system (GIS) information,other geographical information, FCC information regarding the locationof radar transmitters, FCC blacklist information, National Oceanic andAtmospheric Administration (NOAA) databases, Department of Defense (DOD)information regarding radar transmitters, DOD requests to avoidtransmission in DFS channels for a given location, and/or otherregulatory information. Based on the integrated spectral informationand/or the regulation information associated with the data source(s)252, the cloud intelligence engine 235 may select a radio channel fromthe plurality of 5 GHz radio channels for the host access point 218associated with the agility agent 200. Additionally or alternatively,the cloud intelligence engine 235 may select other radio frequencyparameters for the host access point 218 based on the integratedspectral information and/or the regulation information associated withthe data source(s) 252.

The cloud control 1302 may receive control information and/orcoordination information (e.g., authorized and/or preferred channelselection guidance) from the cloud intelligence engine 235. For example,the cloud control 1302 may receive the radio channel selected by thecloud intelligence engine 235. Additionally or alternatively, the cloudcontrol 1302 may receive the other radio frequency parameters selectedby the cloud intelligence engine 235. The agility agent 200 (e.g., thecloud agent 204) may communicate the control information and/or thecoordination information (e.g., the control information and/or thecoordination information received from the cloud intelligence engine235) to the host access point 218 (and/or any other access points withina certain distance from the agility agent 200), enabling direct controlof the host access point 218 by the cloud intelligence engine 235. Forexample, the agility agent 200 (e.g., the cloud agent 204) may thenconfigure the host access point 218 to receive data via the radiochannel selected by the cloud intelligence engine 235 and/or based onthe other radio frequency parameters selected by the cloud intelligenceengine 235. In an alternate implementation, the control agent 1302 maybe employed in an access point not directly connected to the agilityagent 200, or in a peer-to-peer capable mobile device, to enable fasterand/or improved access to DFS channels.

The agility agent 200 may generate the spectral information based on ananalysis of the plurality of 5 GHz radio channels associated with the 5GHz Wi-Fi spectrum 161. For example, the agility agent 200 may switch a5 GHz transceiver (e.g., the primary radio 215) of the agility agent 200to a channel of the plurality of 5 GHz radio channels associated withthe 5 GHz Wi-Fi spectrum 161, generate a beacon in the channel of theplurality of 5 GHz radio channels associated with the 5 GHz Wi-Fispectrum 161, and scan for a radar signal in the channel of theplurality of 5 GHz radio channels associated with the 5 GHz Wi-Fispectrum 161. Additionally, the agility agent 200 may switch a 5 GHztransceiver (e.g., the primary radio 215) of the agility agent 200 toanother channel of the plurality of 5 GHz radio channels associated withthe 5 GHz Wi-Fi spectrum 161, generate a beacon in the other channel ofthe plurality of 5 GHz radio channels associated with the 5 GHz Wi-Fispectrum 161, and scan for a radar signal in the other channel of theplurality of 5 GHz radio channels associated with the 5 GHz Wi-Fispectrum 161. The agility agent 200 may repeat this process for eachchannel of the plurality of 5 GHz radio channels associated with the 5GHz Wi-Fi spectrum 161. The cloud intelligence engine 235 may receivethe spectral information via a wide area network. Furthermore, the cloudintelligence engine 235 may integrate the spectral information withother spectral information generated by the agility agents(s) 251 (e.g.,to generate integrated spectral information). Then, the cloudintelligence engine 235 may determine a radio channel from the pluralityof 5 GHz radio channels based at least on the integrated spectralinformation. In certain implementations, the cloud intelligence engine235 may receive the regulation information from the data source(s) 252.Therefore, the cloud intelligence engine 235 may determine a radiochannel from the plurality of 5 GHz radio channels based on theintegrated spectral information and the regulation informationassociated with the data source(s) 252.

FIG. 14A illustrates an interface between the cloud intelligence engine235, the agility agent 200 and the host access point 218, in accordancewith the present invention. For example, signaling and/or messages maybe exchanged between the cloud intelligence engine 235 and the agilityagent 200. The signaling and/or messages between the cloud intelligenceengine 235 and the agility agent 200 may be exchanged during a DFS scanoperation, during an ISM operation and/or when a radar event occurs thatresults in changing of a radio channel. In an aspect, the signalingand/or messages between the cloud intelligence engine 235 and theagility agent 200 may be exchanged via a WAN (e.g., WAN 234) and/or asecure communication tunnel.

An authentication registration process 1402 of the cloud intelligenceengine 235 may be associated with a message A. The message A may beexchanged between the cloud intelligence engine 235 and the agilityagent 200. Furthermore, the message A may be associated with one or moresignaling operations and/or one or more messages. The message A mayfacilitate an initialization and/or authentication of the agility agent200. For example, the message may include information associated withthe agility agent 200 such as, but not limited to, a unit identity, acertification associated with the agility agent 200, a nearest neighborsscan list associated with a set of other agility agents within a certaindistance from the agility agent 200, service set identifiers, a receivedsignal strength indicator associated with the agility agent 200 and/orthe host access point 218, a maker identification associated with thehost access point 218, a measured location (e.g., a global positioningsystem location) associated with the agility agent 200 and/or the hostaccess point 218, a derived location associated with the agility agent200 and/or the host access point 218 (e.g., derived via a nearby AP or anearby client), time information , current channel information, statusinformation and/or other information associated with the agility agent200 and/or the host access point 218. In one example, the message A canbe associated with a channel availability check phase.

A data fusion process 1404 of the cloud intelligence engine 235 mayfacilitate computation of a location associated with the agility agent200 and/or the host access point 218. Additionally or alternatively, thedata fusion process 1404 of the cloud intelligence engine 235 mayfacilitate computation of a set of DFS channel lists. The data fusionprocess 1404 may be associated with a message B and/or a message C. Themessage B and/or the message C may be exchanged between the cloudintelligence engine 235 and the agility agent 200. Furthermore, themessage B and/or the message C may be associated with one or moresignaling operations and/or one or more messages. The message B may beassociated with spectral measurement and/or environmental measurementsassociated with the agility agent 200. For example, the message B mayinclude information such as, but not limited to, a scanned DFSwhitelist, a scanned DFS black list, scan measurements, scan statistics,congestion information, traffic count information, time information,status information and/or other measurement information associated withthe agility agent 200. The message C may be associated with anauthorized DFS, DFS lists and/or channel change. For example, themessage C may include information such as, but not limited to, adirected (e.g., approved) DFS whitelist, a directed (e.g., approved) DFSblack list, a current time, a list valid time, a computed locationassociated with the agility agent 200 and/or the host access point 218,a network heartbeat and/or other information associated with a channeland/or a dynamic frequency selection.

A network optimization process 1406 of the cloud intelligence engine 235may facilitate optimization of a network topology associated with theagility agent 200. The network optimization process 1406 may beassociated with a message D. The message D may be exchanged between thecloud intelligence engine 235 and the agility agent 200. Furthermore,the message D may be associated with one or more signaling operationsand/or one or more messages. The message D may be associated with achange in a radio channel. For example, the message D may be associatedwith a radio channel for the host access point 218 in communication withthe agility agent 200. The message D can include information such as,but not limited to, a radio channel (e.g., a command to switch to aparticular radio channel), a valid time of a list, a network heartbeatand/or other information for optimizing a network topology.

A network update process 1408 of the cloud intelligence engine 235 mayfacilitate an update for a network topology associated with the agilityagent 200. The network update process 1408 may be associated with amessage E. The message E may be exchanged between the cloud intelligenceengine 235 and the agility agent 200. Furthermore, the message E may beassociated with one or more signaling operations and/or one or moremessages. The message E may be associated with a network heartbeatand/or a DFS authorization. For example, the message E may includeinformation such as, but not limited to, a nearest neighbors scan listassociated with a set of other agility agents within a certain distancefrom the agility agent 200, service set identifiers, a received signalstrength indicator associated with the agility agent 200 and/or the hostaccess point 218, a maker identification associated with the host accesspoint 218, a measured location update (e.g., a global positioning systemlocation update) associated with the agility agent 200 and/or the hostaccess point 218, a derived location update (e.g., derived via a nearbyAP or a nearby client) associated with the agility agent 200 and/or thehost access point 218, time information, current channel information,status information and/or other information. In one example, the messageB, the message C, the message D and/or the message E can be associatedwith an ISM phase.

A manage DFS lists process 1410 of the agility agent 200 may facilitatestorage and/or updates of DFS lists. The manage DFS lists process 1410may be associated with a message F. The message F may be exchangedbetween the agility agent 200 and the host access point 218. In oneexample, the message F may be exchanged via a local area network (e.g.,a wired local area network and/or a wireless local area network).Furthermore, the message F may be associated with one or more signalingoperations and/or one or more messages. The message F may facilitate achange in a radio channel for the host access point 218. For example,the message F may include information such as, but not limited to, anearest neighbors scan list associated with a set of other agilityagents within a certain distance from the agility agent 200, service setidentifiers, a received signal strength indicator associated with theagility agent 200 and/or the host access point 218, a makeridentification associated with the host access point 218, a measuredlocation update (e.g., a global positioning system location update)associated with the agility agent 200 and/or the host access point 218,a derived location update (e.g., derived via a nearby AP or a nearbyclient) associated with the agility agent 200 and/or the host accesspoint 218, time information, current channel information, statusinformation and/or other information. In one example, the message F maybe associated with a cloud directed operation (e.g., a cloud directedoperation where DFS channels are enabled).

FIG. 14B also illustrates an interface between the cloud intelligenceengine 235, the agility agent 200 and the host access point 218, inaccordance with the present invention. For example, FIG. 14B may providefurther details in connection with FIG. 14A. As shown in FIG. 14B,signaling and/or messages may be exchanged between the cloudintelligence engine 235 and the agility agent 200. The signaling and/ormessages between the cloud intelligence engine 235 and the agility agent200 may be exchanged during a DFS scan operation, during ISM and/or whena radar event occurs that results in changing of a radio channel. In anaspect, the signaling and/or messages between the cloud intelligenceengine 235 and the agility agent 200 may be exchanged via a WAN (e.g.,WAN 234) and/or a secure communication tunnel.

As also shown in FIG. 14B, the network update process 1408 of the cloudintelligence engine 235 may facilitate an update for a network topologyassociated with the agility agent 200. The network update process 1408may be associated with the message E. Then, a DFS list update process1414 of the cloud intelligence engine 235 may facilitate an update toone or more DFS channel lists. The DFS list update process 1414 may beassociated with a message G. The message G may be exchanged between thecloud intelligence engine 235 and the agility agent 200. In one example,the message G may be exchanged via a WAN (e.g., WAN 234) and/or a securecommunication tunnel. Furthermore, the message G may be associated withone or more signaling operations and/or one or more messages. Themessage G may be associated with a radar event. For example, the messageG may signal a radar event. Additionally or alternatively, the message Gmay include information associated with a radar event. For example, themessage G may include information such as, but not limited to, a radarmeasurement channel, a radar measurement pattern, a time associated witha radar event, a status associated with a radar event, other informationassociated with a radar event, etc. The radar event may associated withone or more channels from a plurality of 5 GHz communication channels(e.g., a plurality of 5 GHz communication channels associated with the 5GHz Wi-Fi spectrum 161). In one example, the message G can be associatedwith an ISM phase. The DFS list update process 1414 may also beassociated with the message C.

Moreover, as also shown in FIG. 14B, the manage DFS lists process 1410may be associated with the message F. The message F may be exchangedbetween the agility agent 200 and the host access point 218. A radardetection process 1416 of the agility agent 200 may detect and/orgenerate the radar event. Additionally, the radar detection process 1416may notify the host access point 218 to change a radio channel (e.g.,switch to an alternate radio channel). The message F and/or a manage DFSlists process 1412 may be updated accordingly in response to the changein the radio channel. In an aspect, signaling and/or messages may beexchanged between the cloud intelligence engine 235 and the host accesspoint 218 during a DFS scan operation, during an ISM operation and/orwhen a radar event occurs that results in changing of a radio channelfor the host access point 218.

FIG. 15 illustrates an exemplary method 1500 according to the presentinvention for determining the communication channels that will be usedin a plurality of multi-channel DFS masters. First, at 1510 the cloudintelligence engine receives spectral information associated with aplurality of 5 GHz communication channels from a plurality ofmulti-channel DFS masters via one or more network devices. Optionally,at 1511 receiving the spectral information includes receiving scaninformation associated with scanning for a radar signal in the pluralityof 5 GHz radio channels. The spectral information may be generated usingan agility agent device (e.g., agility agent 200 or agility agent 700)based on an analysis of the plurality of 5 GHz communication channels.Analysis of the plurality of 5 GHz communication channels may includeswitching a 5 GHz radio transceiver of the agility agent device to achannel of the plurality of 5 GHz communication channels, generating abeacon in the channel of the plurality of 5 GHz communication channels,and scanning for a radar signal in the channel of the plurality of 5 GHzcommunication channels. The spectral information may include informationsuch as, for example, a whitelist (e.g., a whitelist of each of theplurality of 5 GHz communication channels that does not contain a radarsignal), a blacklist (e.g., a blacklist of each of the plurality of 5GHz communication channels that contains a radar signal), scaninformation associated with a scan for a radar signal in the pluralityof 5 GHz communication channels, state information, location informationassociated with the agility agent device and/or the access point device,time signals, scan lists (e.g., scan lists showing neighboring accesspoints, etc.), congestion information (e.g., number of re-try packets,type of re-try packets, etc.), traffic information and/or other spectralinformation. Next, at 1520, the cloud intelligence engine integrates thespectral information with other spectral information to generateintegrated spectral information. The other spectral information maygenerated by at least one other agility agent device. In one example,the spectral information may be integrated with the other spectralinformation via one or more data fusion processes.

Then, at 1530, the cloud intelligence engine determines thecommunication channels for the plurality of multi-channel DFS mastersfrom the plurality of 5 GHz communication channels based at least on theintegrated spectral information. For example, a communication channelmay be selected from the plurality of 5 GHz communication channels basedat least on the integrated spectral information. In an aspect,regulation information associated with the plurality of 5 GHzcommunication channels and/or stored in at least one database may bereceived by the cloud intelligence device. Furthermore, thecommunication channel may be further determined based on the regulationinformation. In another aspect, an indication of the communicationchannel may be provided to the agility agent device and/or the accesspoint device.

FIG. 16 illustrates an exemplary method 1600 according to the presentinvention for determining the communication channels that will be usedin a plurality of multi-channel DFS masters. The method illustrated inFIG. 16 includes the steps described in relation to FIG. 15 above butalso includes the following optional additional steps. At 1610, themethod includes transmitting a whitelist of each of the plurality of 5GHz radio channels that does not contain a radar signal to the pluralityof multi-channel DFS masters via the one or more network devices. At1620 the method includes transmitting a blacklist of each of theplurality of 5 GHz radio channels that contains a radar signal to theplurality of multi-channel DFS masters via the one or more networkdevices. At 1630 the method includes receiving regulation informationstored in at least one database. The regulation information may include,but is not limited to, GIS information, other geographical information,FCC information regarding the location of radar transmitters, FCCblacklist information, NOAA databases, DOD information regarding radartransmitters, DOD requests to avoid transmission in DFS channels for agiven location, and/or other regulatory information. At 1640, the methodmay include determining the communication channels based on theintegrated spectral information and the regulation information.

As discussed herein, the disclosed systems are fundamentally differentfrom the current state of art in that: (a) the disclosed wirelessagility agents enable multiple simultaneous dynamic frequency channels,which is significantly more bandwidth than provided by conventionalstandalone DFS master access points or small cell base stations; (b) theadditional DFS channels may be shared with nearby (suitably equippedwith a control agent) access points or small cells, enabling the networkas a whole to benefit from the additional bandwidth; and (c) theselection of operating channels by the access points and/or small cellbase stations can be coordinated by a centralized network organizationelement (the cloud intelligence engine) to avoid overlapping channelsthus avoiding interference and relieving congestion.

The capability and functions in (a) to (c) are enabled by thecentralized cloud intelligence engine which collects and combines theDFS radar and other spectrum information from each agility agent andgeo-tags, stores, filters, and integrates the data over time, andcombines it together by data fusion technique with information from aplurality of other agility agents distributed in space, and performsfiltering and other post-processing on the collection with proprietaryalgorithms, and merges with other data from vetted sources (such as GIS,Federal Aviation Administration (FAA), FCC, and DoD databases, etc.).

Specifically, the cloud intelligence engine performs the following:continuously collects the spectrum, location and networkcongestion/traffic information from all wireless agility agents, thenumber and density of which grows rapidly as more access points andsmall cell base stations are deployed; continuously applyingsophisticated filtering, spatial and time correlation and integrationoperations, and novel array-combining techniques, and patternrecognition, etc. across the data sets; applying inventive networkanalysis and optimization techniques to compute network organizationdecisions to collectively optimize dynamic channel selection of accesspoints and small cell base stations across networks; and directing theadaptive control of dynamic channel selection and radio configuration of802.11 a/n/ac access points and/or LTE-U small cell base stations viasaid wireless agility agents.

Agility agents, due to their attachment to Wi-Fi access points and LTE-Usmall cell base stations, are by nature deployed over wide geographicalareas in varying densities and often with overlapping coverage. Thus thespectrum information collected by agility agents, in particular thesignatures of DFS radar and congestion conditions of local networks,similarly represent multi-point overlapping measurements of the radiospectrum over wide areas, or viewed a different way, the informationrepresents spectrum measurements by random irregular arrays of sensorsmeasuring radar and sources of interference and/or congestion fromdifferent angles (see FIG. 17).

FIG. 17 illustrates how multiple agility agents 1711, 1712, 1713, 1714(for example, each attached to an 802.11 a/n/ac Wi-Fi network) providegeographically distributed overlapping views (sets of sensor data) of aradar emitter 1750. The FIG. also shows how by reporting to thecentralized cloud intelligence engine 235, the collective multiple viewdata when pieced together by the cloud intelligence engine 235 takes onthe attributes of both spatial diversity (different range andfading/reflective channel conditions 1721, 1722, 1723, 1724) and angulardiversity (for example, look angles 1731, 1732, 1733, 1734) all of whichcan thus be leveraged to generate a pseudo synthetic aperture view ofthe target radar 1750 or any other emitter source with considerably moreeffective gain and sensitivity than was represented by any single viewfrom a single access point or small cell base station. Differentpositions 1721, 1722, 1723, 1724 and look angles 1731, 1732, 1733, 1734results in different timing offset of received radar pulse train anddifferent distortion of received signal due to different fading andreflective channel conditions. A subset of the agility agents 1711,1712, 1713, 1714 may form a pseudo-synthetic antenna array that providesimproved sensitivity to radar signals due to effective higher gain androbustness in radar detection due to redundancy. The data from theagility agents 1711, 1712, 1713, 1714 are transmitted to the cloudintelligence engine 235 which performs data correlation and integrationto determine the location of the target radar 1750.

The cloud intelligence engine having considerable processingcapabilities and infinitely scalable memory/storage, is able to storethe time-stamped spectrum information from each agility agent over verylong periods of time, thus enabling the cloud intelligence engine toalso integrate and correlate the signatures of DFS radar and congestionconditions of the local network over time as well as over geographicspace. Given a sufficient number of agility agents continuouslyacquiring spectral information over time, the cloud intelligence enginecan construct an increasingly accurate and reliable spatial map ofspectrum information in the 5 GHz band, including the presence orabsence of radar signals. The spectral information may belocation-tagged and/or time-stamped. The device may be, for example, anaccess point device, a DFS slave device, a peer-to-peer group ownerdevice, a mobile hotspot device, a radio access node device or adedicated sensor node device. With this information, client devices candirectly query the cloud intelligence engine to find out what DFSchannels are available and free of radar at the location of the clientdevice. With this system, the client device no longer needs to wait fora beacon that would have otherwise been provided by an access point oragility agent as the client device can communicate with the cloudintelligence engine via a network connection to determine the availablechannels. In this situation, the cloud intelligence engine becomes acloud DFS super master as it can provide DFS channel selectioninformation for a plurality of client devices distributed over a widerange of geographies.

Further, the cloud intelligence engine is also able to access andcombine data from other sources (data fusion), such as topographic andmap information from GIS (Geographical Information System) servers, FCCdatabases, NOAA databases, etc. enabling the cloud intelligence engineto further compare, correlate, overlay and otherwise polish the baselinespectrum data from agility agents and augment the networkself-organization algorithm to further improve the overall accuracy androbustness of the invention.

The cloud intelligence engine having thus formed a detailed picture ofthe dynamic spectrum conditions of 802.11 a/n/ac and LTE-U networks isable to use this data to compute optimal network configurations, inparticular the selection of operating channels (in both DFS and non-DFSbands) and radio parameters, of individual access points and/or smallcell base stations to avoid overlap with other nearby access points orbase stations, interferers, and noisy or congested channels. The overallsystem embodied by this can thus be viewed as a large wide-area closedcontrol system, as illustrated in FIG. 18.

In one example, a system of the present invention includes a cloud DFSsuper master and a plurality of radar detectors communicatively coupledto the cloud DFS super master. The radar detectors are programmed toscan for a radar signal in each of a plurality of 5 GHz radio channels,to transmit the results of the scan for the radar signal to the cloudDFS super master, and to transmit geo-location information for each ofthe plurality of radar detectors to the cloud DFS super master. Thecloud DFS super master is programmed to receive the results of the scanfor the radar signal from each of the plurality of radar detectors andthe geo-location information for the plurality of radar detectors anddetermine if a first radar detector of the plurality of radar detectorsdetected the radar signal in a first channel of the plurality of 5 GHzradio channels. If the cloud DFS super maser determines that the radarsignal is present in the first channel, the cloud DFS super master isprogrammed to determine one or more radar detector (e.g., second radardetectors) of the plurality of radar detectors to evaluate the firstradar detector's detection of the radar signal in the first channelbased on the geo-location information for the first radar detector andthe geo-location for the second radar detector. In one example, thecloud DFS super master is programmed to cause the one or more secondradar detectors to switch to the first channel and scan for radar in thefirst channel. And in another example, the cloud DFS super master isprogrammed to cause the one or more second radar detectors increase adwell time in the first channel. In these examples, the cloud DFS supermaster can coordinate the radar detectors when any one detector seesradar. The cloud DFS super master and network of radar detectors actslike a large synthetic aperture array, and the cloud DFS super mastercan control the radar detectors to take action. Some of the actionsinclude moving one or more radar detector to the channel in which radarwas detected and looking for radar or causing one or more radardetectors to dwell longer in the channel in which radar was detected.The more sensors looking at the radar signal, the better the radarsignal can be characterized. Further, through geo-location the cloud DFSsupertaster may determine that there are other detectors in a betterposition to measure or characterize the radar and may use data from oneor more detectors (e.g., fusing data from multiple detectors). Thiscould be driven by historical data or by knowing the type/model ofdetectors. Indeed, as sensors are upgraded their sensitivity may bebetter than previous generation of products. The cloud DFS supertastermay track what detectors (and their capabilities) are deployed in agiven area and optimally select which ones will provide the secondaryverifying radar scans.

FIG. 18 illustrates in a control loop diagram how the cloud intelligenceengine takes the spectrum data (radar lists and patterns, whitelists,blacklists, RSSI, noise floor, nearest neighbors, congestion & trafficsignatures, etc.) from a network of agility agents (e.g., each of theglobal network of agility agents 1810), and after storing (in storage1825) and filtering the data, combines them with similar data from anagility agent 1811, cloud data 1820 from other sources (such as the GIS,FCC, FAA, DoD, NOAA, etc.), and user input 1835. Then applying the datato the network self-organization compute process 1826, the control loopperforms optimum dynamic channel selection 1855 for each of the 802.11a/n/ac access points or LTE-U small cell base stations in the network(s)and under control of the system embodied by this invention. In this way,the cloud intelligence engine tells the agility agent 1811 to change tothe selected channel 1855 for the access point (using access pointcontrol 1812) from the current channel 1856 (the channel previously usedby the access point). In contrast, conventional access points and smallcell base stations behave as open control loops with limitedsingle-source sensor input and without the benefit of the cloudintelligence engine to close the control loop.

Information (including spectral and location information) from theagility agent 1811 is used with information from a location database1851 to resolve the location 1850 of the agility agent 1811 and the802.11 a/n/ac access points or LTE-U small cell base stations in thenetwork(s) and under control of the agility agent 1811. The lookup 1841accesses stored data from the agility agents 1810. This information canbe combined with the information from the resolve location step 1850 forgeometric extrapolation 1842 of spectral conditions applicable foragility agent 1811 and the 802.11 a/n/ac access points or LTE-U smallcell base stations in the network(s) and under control of the agilityagent 1811.

As illustrated in FIG. 18, the control loop includes time integration ofdata 1845 from the agility agents 1811, spatial integration of data 1844from the agility agents 1811, and fusion 1830 with data from othersources and user input 1835 to make an operating channel selection 1855for agility agent 1811. As shown, the control loop also may includebuffers 1847, 1849 (temporal), 1843 (spatial), 1846 (temporal) andfilters 1848 as needed. The other agility agents 1810 may also havetheir own control loops similar to that illustrated in FIG. 18.

As previously discussed, the agility agent transmits information to thecloud intelligence engine including information about the detected radarpattern including signal strength, type of radar, and a time stamp forthe detection. The type of radar detected includes information such asburst duration, number of bursts, pulses per burst, burst period, scanpattern, pulse repetition rate and interval, pulse width, chirp width,beam width, scan rate, pulse rise and fall times, frequency modulation,frequency hopping rate, hopping sequence length, and pulses per hop. Thecloud intelligence engine uses this information to improve its falsedetection algorithms. For example, if an agility agent detects aparticular radar type that it knows cannot be present in a certainlocation, the cloud intelligence engine can use that information in itprobability algorithm for assessing the validity of that signal. Theagility agent may transmit information to the cloud intelligence enginevia an access point or via a client device as shown in FIG. 2.

Because the cloud intelligence engine has location information for theattached radar sensors, when the cloud intelligence engine receives aradar detection signal from one sensor, the cloud intelligence enginemay use the location information for that sensor to verify the signal.The cloud intelligence engine may determine nearby sensors in thevicinity of the first sensor that detected the radar signal and searchfor the whitelist/blacklist channel history in the other sensors, and ifthe nearby sensors have current and sufficient information, the cloudintelligence engine may validate or invalidate the original radardetection from the first sensor.

Alternatively, the cloud intelligence engine or the first sensor mayinstruct nearby sensors (either through the cloud or locally) to focuson the detected channel and report their whitelist and blacklist back tothe cloud. If the nearby sensors have current and sufficientinformation, the cloud intelligence engine may validate or invalidatethe original radar detection from the first sensor. Further, based onthe location information for the first sensor, the cloud intelligenceengine may direct other nearby sensors to modify their scan times orcharacteristics or signal processing to better detect the signaldetected by the first sensor.

As described above FIGS. 14A and 14B illustrates the logical interfacebetween the wireless agility agent, the cloud intelligence engine, andan access point (or similarly a small cell LTE-U base station). Inparticular FIGS. 14A-14B also illustrate examples of the signaling andmessages that can be exchanged between the agility agent and the cloudintelligence engine, and between the cloud intelligence engine and anaccess point (via the agility agent) during the phases of DFS scanoperations, In-Service Monitoring (ISM) and when a radar event occursforcing a channel change. In addition to traditional infrastructurenetwork topologies (e.g., host Access point and clients and peer-to-peernetworks or Wi-Fi-Direct), the present inventions apply to extendedinfrastructure network topologies (e.g., mesh networks). For example,the host access points discussed herein could be a mesh peerparticipating in a mesh network and simultaneously providinginfrastructure connectivity.

FIG. 19A illustrates the hidden node problem where an access points orsmall cell base station 1930 is hidden from view of other access pointsor small cell base stations 1931 by topography, obstruction, distance orchannel conditions 1945. The hidden node problem is a particularlydifficult issue with mesh networks or peer-to-peer sessions where theseaccess points are communicating with each other; the hidden node 1930may not detect the frame and would be unable to synchronize its networkallocation vector (NAV). With this impairment the hidden node 1930transmissions can potentially collide and interfere with communicationsbetween the other two nodes 1931, 1932. As shown in FIG. 19A, theagility agent 1950 reports scan lists to the cloud intelligence engine1935 but cannot detect the hidden node 1930. Accordingly, the agilityagent 1950 does not report the hidden node 1930 to the cloudintelligence engine 1935 in the reported scan lists. Agility agents 1951associated with access points 1932 in neighboring networks also reportscan lists to the cloud intelligence engine 1935. Because the hiddennode 1930 may be detected by these agility agents 1951, the reportedscan lists include the hidden node 1930. The cloud intelligence engine1935 collects scan lists, from all agility agents 1950, 1951 includinggeographic information about the agility agents 1950, 1951. The cloudintelligence engine 1935 then determines the presence of the hidden node1930 and reports the presence of the hidden node 1930 to agility agents1950, 1951.

FIG. 19B illustrates the hidden radar problem, where a radar emitter1960 is unseen by an agility agent 1953 due to topography or obstruction1955. The hidden radar problem is a very serious concern of the FCC (andother regulators) because agility agent 1953 acting as a DFS masterdevice for access points 1934 but not seeing the hidden radar 1960 maycause unintended interference. Agility agents 1952 near exposed nodes1933 detect radar from a radar emitter 1960 and report to the cloudintelligence engine 1935 via an uplink back list message for example.The cloud intelligence engine 1935 informs agility agents 1953 nearhidden nodes 1934 of the radar via a downlink black list message forexample.

In some embodiments, an agility agent may be linked to multiple hostaccess points. In one such possible configuration, a significant issuearises when the networking connection between the agility agent and anaccess point over Ethernet is long. FIG. 19C illustrates the hiddenradar problem where a radar emitter is unseen by an agility agent due todistance. Networked nodes 1990, 1991, 1992 are far from a radar emitter1975 and therefore do not detect the presence of radar signals. Thenodes 1990, 1991, 1992 communicate this information to the agility agent1970. The agility agent 1970 causes corresponding whitelists and blacklists to be broadcast wirelessly and over wired connections. A hiddennode 1980 receives the lists from the agility agent 1970 but is in thepresence of radar from the radar emitter 1975. The hidden node 1980 isseparated from the agility agent 1970 by a long distance and isconnected to the agility agent by a very long Ethernet connection 1981for example.

Because the hidden node 1980 is distant from the agility agent 1970, itssignature 1982 is not on the agility agent's 1970 scan list. Also,because the hidden node 1980 is too distant from the agility agent 1970,the hidden node 1980 cannot receive the wireless whitelist and/or blacklist from the agility agent 1970 or the time stamps of the wirelesslists do not match those received via Ethernet when received by thehidden node 1980. To solve this problem, the whitelists and/or blacklists broadcast over wired Ethernet must match with the lists and timingbroadcast over wireless in order for the node 1980 to use DFS channels.Also, the agility agent 1970 may broadcast list of authorized accesspoints (e.g., 1990, 1991, 1992), and the control agent in the accesspoint must see its SSID in the authorization list in order to use DFSchannels. The agility agent 1970 only authorizes access points (e.g.,1990, 1991, 1992) which it sees by scan list and above a certain RSSIthreshold. Access points 1980 who are not seen or have RSSI too low aredeemed too far to use the agility agent's 1970 whitelist.

FIG. 19A-C illustrate how a cloud intelligence engine collecting datafrom a plurality of wireless agility agents proximal to the hidden nodeor hidden radar is able to discover the said hidden node or hiddenradar. Any access point or small cell base station that is now awarethat there is a hidden node to another access point on the same channelcan now react to the node being hidden, and similarly any (and all)access points or small cells within probable range of a radar signal,even though hidden to some of the nodes, could be directly preventedfrom using a radar-occupied channel.

In one embodiment of a system using a cloud DFS super master, the cloudDFS super master receives information from a plurality of agility agentsand/or access points. Additionally, because the cloud DFS super masterprovides the DFS channel information for client devices, some agilityagents and access points will no longer need to transmit a beaconidentifying available channels. In this situation, the system using acloud DFS super master may include sensors that are radar detectors thatperform the radar-sensing function of the agility agent described hereinbut do not transmit a beacon to identify the available channels.

The cloud DFS super master may provide the DFS super master function fora region for which the cloud DFS super master has sufficientinformation. For example, if agility agents and/or radar detectingsensors are distributed with a sufficient density in a given localityand the cloud DFS super master has received enough information forenough time for the locality to determine the radar signal signature forthe locality with enough certainty to comply with FCC or otherapplicable requirements, the cloud DFS super master may provide DFSmaster services for devices located in the locality.

With a cloud DFS super master system, traditional DFS masters andagility agents can be eliminated or operate as sensors that continue todo radar detection, but do not tell client devices what channels to use.In this system, client devices do not have to look for a beacon, butinstead can query the cloud DFS super master to determine what channelsare available to use.

This cloud DFS super master systems solve several problems inherent toprior-art DFS master systems. For example, the cloud DFS super mastersystem may receive information from external sources (such astopographic and map information from GIS servers, FCC databases, NOAAdatabases, DoD databases) that the cloud DFS super master uses togeo-fence an area from DFS communications in one or more channels. Inone example, the DoD instructs the cloud DFS super master to preventcommunications in the DFS spectrum in a given area for a time period.The cloud DFS super master system would instruct client devices not touse the DFS spectrum when the devices are in that area. In anotherexample, the cloud DFS super master is programmed to receive a requestto vacate one or more 5 GHz radio channels from a priority user. Apriority user can be a radar producer that includes a system of a radarproducing entity such as an airport or military body, or the priorityuser can be a government or emergency entity that needs priority accessto the DFS spectrum. In this example, the cloud DFS super master is alsoprogrammed to transmit a message to the client devices within theaffected areas of the request instructing the client devices to vacatethe 5 GHz radio channels in response to the request from the priorityuser. Using this system, an airplane or airport could request the cloudDFS super master to block out a 5 GHz channel along its route as it istaking off. In another embodiment, the request to vacate one or more 5GHz radio channels could come from governmental, regulatory, oremergency systems. For example, an ambulance or other emergency vehiclecan send real time requests to the cloud DFS super master to block out a5 GHz channel along its route in order to optimize communications forthe emergency vehicle. Current beaconing systems cannot efficientlysolve this problem unlike the disclosed cloud DFS super master. Thecloud DFS super master can further receive and use location informationfor the priority user to dynamically change the area in which the DFSsuper master instructs devices to vacate the channel(s) requested by thepriority user. This allows the DFS super master to geo-fence a limitedarea to maximize the availability of the DFS channels to other deviceswhile still complying with the request to vacate from the priority user.

Additionally, the cloud DFS super master systems addresses currentlimitations of use of the DFS spectrum. Currently, many DFS masterdevices are private access points that only provide access to the DFSspectrum to member client devices. Accordingly, most users in the areacannot utilize the available DFS spectrum because they are not membersof the group with access to the access point acting as the DFS master.In this situation, even though the DFS spectrum is unlicensed andgenerally available to the public for use, only a select group withaccess to the private access point can use the DFS spectrum. The cloudDFS super master addresses this inefficiency by providing DFS channelavailability information directly to client devices in any area forwhich the cloud DFS super master has sufficient spectral information.

Further, the cloud DFS super master systems addresses problems withproliferation of LTE-U devices and interoperability of LTE-U devices andWi-Fi devices. LTE-U devices use the same bands as Wi-Fi devices.However, Wi-Fi devices cannot detect LTE-U devices, and LTE-U devicescannot detect Wi-Fi devices. Consequentially, signals from LTE-U andproximate Wi-Fi devices collide and interfere with each other. The cloudDFS super master can control the timing and frequencies used byconnected devices. And because the cloud DFS super master can see all ofthe client devices—including LTE-U and Wi-Fi devices—the cloud DFS supermaster can coordinate traffic to mitigate collisions for, by example,making sure that two devices in the same area are not on the samechannel. The cloud DFS super master addresses the issue of proximateLTE-U and Wi-Fi devices without a need for the LTE-U and Wi-Fi devicesto talk to each other.

Also, as discussed above, the cloud DFS super master solves the hiddennode issue. And the cloud DFS super master can coordinate traffic amongclient devices.

In one embodiment of the cloud DFS super master system, the cloud DFSsuper master is connected to an access point that receives channelselection information from the cloud DFS super master (such as awhitelist or blacklist) and transmits beacons according to the receivedchannel selection information. In this case the cloud DFS super masterstill controls the channel selection for the access point.

FIG. 20 illustrates an exemplary embodiment of the cloud DFS supermaster system 2000 in which the cloud intelligence engine 2035 operatesas a cloud DFS super master. In the system 2000, the cloud DFS supermaster 2035 is communicatively coupled to a plurality of sensors 2050,2051, 2052 that detect radar signals in the DFS band and detect wirelesstraffic information. The plurality of sensors 2050, 2051, 2052 may be inagility agents or may be standalone sensors. In one example, thestandalone sensor includes a power supply and is self-contained in anenclosure and comprises a self-contained plug-in device. The sensors'communication with the cloud DFS super master 2035 may be continuous orintermittent. The sensors transmit information about detected radarsignals, traffic information, and geo-location information for thesensor to the cloud DFS super master 2035. The cloud DFS super master2035 may also be connected to external data sources 2060 such astopographic and map information from GIS servers, FCC databases, NOAAdatabases, DoD databases. The cloud DFS super master 2035 uses theinformation from the sensors 2050, 2051, 2052 and the external databases2060 to determine available DFS channels for the areas for which thecloud DFS super master has sufficient information. Then as shown in FIG.20, client devices 2080, 2081 then connect to the cloud DFS super master2035 to request authorized DFS channels according to the location of theclient devices 2080, 2081. The client devices 2080, 2081 transmitgeo-location information to the cloud DFS super master 2035 so that thecloud DFS super master 2035 can determine the appropriate channels basedon that location information.

In one embodiment, the cloud DFS super master system is a system fordetecting radar signals and avoiding interference with the radar signalsthat includes a cloud DFS super master, a plurality of radar detectors,and at least one client device. The plurality of radar detectors (orradar sensors) are communicatively coupled to the cloud DFS super masterand programmed to scan for a radar signal in each of a plurality of 5GHz radio channels, to transmit the results of the scan for the radarsignal to the cloud DFS super master, and to transmit geo-locationinformation for each of the plurality of radar detectors to the cloudDFS super master. The client device (or client devices) iscommunicatively coupled to the cloud DFS super master and programmed totransmit geo-location information for the client device and a requestfor available 5 GHz radio channels to the cloud DFS super master. Thecloud DFS super master is programmed to receive the results of the scanfor the radar signal from each of the plurality of radar detectors, thegeo-location information for the plurality of radar detectors, thegeo-location information for the client device and the request foravailable 5 GHz radio channels and is programmed to determine one ormore 5 GHz radio channels that are free of radar signals within adistance of the client device from the results of the scan for the radarsignal from each of the plurality of radar detectors, the geo-locationinformation for the plurality of radar detectors, and the geo-locationinformation for the client device and to transmit the one or more 5 GHzradio channels that are free of radar signals within a distance of theclient device to the client device.

In another embodiment, the cloud DFS super master is programmed toreceive information from an external data source and is programmed todetermine which of the one or more 5 GHz radio channels that are free ofradar signals within a distance of the client device from theinformation from the external data source and the results of the scanfor the radar signal from each of the plurality of radar detectors, thegeo-location information for the plurality of radar detectors, and thegeo-location information for the client device. The external data sourcecan be a GIS, an FAA radar database, a DoD radar database, an FCCdatabase, or a NOAA database for example.

Along with radar detection information, the plurality of radar detectorsmay be programmed to transmit wireless spectrum information (such astraffic, congestion, channels used by proximate access points) to thecloud DFS super master and the cloud DFS super master is programmed tocoordinate transmissions of the client device. This way, the cloud DFSsuper master can coordinate traffic for several devices including accesspoints to reduce congestion and collisions from using the same channelat the same time. The cloud DFS super master may apply time divisionand/or frequency division coordination to improve the client devices'performance.

FIG. 21 provides an illustration of a standard peer-to-peer network2190. As shown in FIG. 21, an access point 2101 such as a wirelessaccess point is connected to a wide area network (WAN) 2110. The accesspoint 2101 provides wireless access to the wide area network 2110 to theclient devices 2120, 2121, 2122, 2123. The client devices 2120, 2121,2122, 2123 also form a peer-to-peer network 2100 through which theclient devices 2120, 2121, 2122, 2123 can communicate with each otherwithout utilizing the access point 2101. Note that in thisconfiguration, the client devices 2120, 2121, 2122, 2123 cannot use DFSchannels to communicate with each other because they do not havesufficient information about available DFS channels to allow DFScommunication that complies with regulatory standards.

FIG. 22 illustrates an embodiment of the present invention in which aDFS master 2200 is coupled to a cloud intelligence engine 2255 andprovides available DFS channels to client devices 2220, 2221, 2222,2223. As show in this illustration, the DFS master 2200 includes areceiver 2202 and a transmitter 2203. The DFS master 2200 providesspectral information to the cloud intelligence engine 2255. Further, thecloud intelligence engine 2255 is coupled to other DFS masters 2250 thatprovide additional spectral information to the cloud intelligence engine2255. The DFS master 2200 may broadcast a beacon to client devices 2220,2221, 2222, 2223 to notify the client devices 2220, 2221, 2222, 2223 ofthe available DFS channels. Also, the DFS master 2200 can connect to oneor more of the client devices 2220, 2221, 2222, 2223 via an installedapplication on the client devices 2220, 2221, 2222, 2223, for example,to communicate the available DFS channels. Alternatively, the cloudintelligence engine 2255 can connect to one or more of the clientdevices 2220, 2221, 2222, 2223 via an installed application on theclient devices 2220, 2221, 2222, 2223, for example, to communicate theavailable DFS channels. Using the available DFS channels, the clientdevices 2220, 2221, 2222, 2223 can communicate directly with each otherin a peer-to-peer network 2290.

FIG. 23 illustrates another embodiment of the present invention in whichthe DFS master 2300 is not directly connected to the cloud intelligenceengine 2355. As show in this illustration, the DFS master 2300 includesa receiver 2302 and a transmitter 2303. The DFS master 2300 providesspectral information to the cloud intelligence engine 2355. Further, thecloud intelligence engine 2355 is coupled to other DFS masters 2350 thatprovide additional spectral information to the cloud intelligence engine2355. The DFS master 2300 connects to and communicates with the cloudintelligence engine 2355 via a network connection in the client devices2321, 2322. In this illustration, the DFS master 2300 connects to andcommunicates with the cloud intelligence engine 2355 via a networkconnection in two client devices 2321, 2322, but the DFS master 2300 mayconnect to and communicate with the cloud intelligence engine 2355 via anetwork connection in one or more client devices. Via this connection,the DFS master 2300 transmits spectral information to the cloudintelligence engine 2355 and receives DFS channel availabilityinformation from the cloud intelligence engine 2355. The DFS master 2300may broadcast a beacon to client devices 2320, 2321, 2322, 2323 tonotify the client devices 2320, 2321, 2322, 2323 of the available DFSchannels. Also, the DFS master 2300 can connect to one or more of theclient devices 2320, 2321, 2322, 2323 via an installed application onthe client devices 2320, 2321, 2322, 2323, for example, to communicatethe available DFS channels. Alternatively, the cloud intelligence engine2355 can connect to one or more of the client devices 2321, 2322 via aninstalled application on the client devices 2321, 2322, for example, tocommunicate the available DFS channels. Using the available DFSchannels, the client devices 2320, 2321, 2322, 2323 can communicatedirectly with each other in a peer-to-peer network 2390.

FIG. 24 provides a more-detailed illustration of an exemplary system ofthe present invention. As illustrated in FIG. 24, the cloud intelligenceengine 2435 may be connected to a plurality of DFS masters 2400 andclient or user devices 2431, 2432 that form a peer-to-peer network. Thepeer-to-peer devices 2431, 2432 may have a user control interface 2428.The user control interface 2428 includes a user interface 2429 to allowthe client devices 2431, 2432 to interact with the DFS master 2400 viathe cloud intelligence engine 2435. For example, the user interface 2429may allow the user to modify DFS master 2400 settings. The user controlinterface 2428 also includes a security element 2430 to ensure thatcommunications between the client devices 2431, 2432 and the DFS master2400 are secure. The client devices 2431, 2432 are connected to a widearea network 2434 via a cellular network for example.

The cloud intelligence engine 2435 includes a database 2448 and memory2449 for storing information from the DFS master 2400, one or more otherDFS masters connected to the cloud intelligence engine 2435 and/or oneor more external data source (e.g., data source(s) 2452). The database2448 and memory 2449 allow the cloud intelligence engine 2435 to storeinformation associated with the DFS master 2400, the other DFS master(s)and/or the data source(s) 2452 over a certain period of time (e.g.,days, weeks, months, years, etc.). The data source(s) 2452 may beassociated with a set of databases. Furthermore, the data source(s) 2452may include regulatory information such as, but not limited to, GISinformation, other geographical information, FCC information regardingthe location of radar transmitters, FCC blacklist information, NOAAdatabases, DOD information regarding radar transmitters, DOD requests toavoid transmission in DFS channels for a given location, and/or otherregulatory information.

The cloud intelligence engine 2435 also includes processors 2450 toperform the cloud intelligence operations described herein. In anaspect, the processors 2450 may be communicatively coupled to the memory2449. Coupling can include various communications including, but notlimited to, direct communications, indirect communications, wiredcommunications, and/or wireless communications. In certainimplementations, the processors 2450 may be operable to execute orfacilitate execution of one or more of computer-executable componentsstored in the memory 2449. For example, the processors 2450 may bedirectly involved in the execution of the computer-executablecomponent(s), according to an aspect. Additionally or alternatively, theprocessors 2450 may be indirectly involved in the execution of thecomputer executable component(s). For example, the processors 2450 maydirect one or more components to perform the operations.

The cloud intelligence engine 2435 also knows the location of each DFSmaster and the access points proximate to the DFS masters that do nothave a controlling agent as well as the channel on which each of thosedevices is operating. With this information, the spectrum analysis anddata fusion engine 2443 and the network optimization self-organizationengine 2444 can optimize the local spectrum by telling DFS masters toavoid channels subject to interference. The swarm communications manager2445 manages communications between DFS masters, access points, clientdevices, and other devices in the network. The cloud intelligence engineincludes a security manager 2446. The control agents manager 2447manages all connected control agents.

The cloud intelligence engine 2435 may combine the spectral informationwith other spectral information (e.g., other spectral informationassociated with DFS master(s)) to generate combined spectralinformation. Then, the cloud intelligence engine 2435 may determine oneor more particular communication channels (e.g., a particularcommunication channel associated with the 24 GHz Wi-Fi spectrum 291) andmay communicate the particular communication channels to the DFS master2400 (e.g., via a secure communications tunnel through the clientdevices 2431, 2432). The DFS master 2400 and/or the cloud intelligenceengine 2435 use the information from the cloud intelligence engine 2435to determine the DFS channels to make available to client devices 2431,2432.

Independent of any host access point, the DFS master 2400, in the roleof an autonomous DFS master device, may provide the channel indicationand channel selection control to one or more peer-to-peer client devices2431, 2432 within the coverage area by (a) signaling availability of oneor more DFS channels by simultaneous transmission of one or more beaconsignals; (b) transmitting a listing of both the authorized available DFSchannels, herein referred to as a whitelist and the prohibited DFSchannels in which a potential radar signal has been detected, hereinreferred to as a blacklist along with control signals and a time-stampsignal, herein referred to as a dead-man switch timer via an associatednon-DFS channel; and (c) receiving control, coordination and authorizedand preferred channel selection guidance information from the cloudintelligence engine 2435.

The capability and functions in (a) to (c) are enabled by thecentralized cloud intelligence engine which collects and combines theDFS radar and other spectrum information from each DFS master andgeo-tags, stores, filters, and integrates the data over time, andcombines it together by data fusion technique with information from aplurality of other DFS masters distributed in space, and performsfiltering and other post-processing on the collection with proprietaryalgorithms, and merges with other data from vetted sources (such asGIS—Geographical Information System, FAA, FCC, and DoD databases, etc.).

Specifically, the cloud intelligence engine performs the following; (a)continuously collects the spectrum, location and networkcongestion/traffic information from all wireless DFS masters, the numberand density of which grows rapidly as more access points and small cellbase stations are deployed; (b) continuously applies sophisticatedfiltering, spatial and time correlation and integration operations, andnovel array-combining techniques, and pattern recognition, etc. acrossthe data sets; (c) applies inventive network analysis and optimizationtechniques to compute network organization decisions to collectivelyoptimize dynamic channel selection of access points and small cell basestations across networks; and (d) directs the adaptive control ofdynamic channel selection and radio configuration of said wireless DFSmasters.

In the illustrated example, the DFS master 2400 includes a primary radio2415 and a secondary radio 2416. The primary radio 2415 is for DFS andradar detection. The primary radio 2415 is typically a 24 GHz radio. Inone example, the primary radio 2415 can be a 24 GHz transceiver. The DFSmaster 2400 may receive radar signals, traffic information, and/orcongestion information through the primary radio 2415. And the DFSmaster 2400 may transmit information, such as DFS beacons, via theprimary radio 2415. The secondary radio 2416 is a secondary radio forsending control signals to other devices in the network. The secondaryradio 2416 is typically a 21.4 GHz radio. The DFS master 2400 mayreceive information such as network traffic, congestion, and/or controlsignals with the secondary radio 2416. And the DFS master 2400 maytransmit information, such as control signals, with the secondary radio2416. The primary radio 2415 is connected to a fast channel switchinggenerator 2417 that includes a switch and allows the primary radio 2415to switch rapidly between a radar detector 2411 and beacon generator2412. The fast channel switching generator 2417 allows the radardetector 2411 to switch sufficiently fast to appear to be on multiplechannels at a time.

The standalone multi-channel DFS master may include a beacon generator2412 to generate a beacon in each of a plurality of 24 GHz DFS radiochannels (e.g., a plurality of 24 GHz DFS radio channels associated withthe 24 GHz Wi-Fi spectrum 291), a radar detector 2411 to scan for aradar signal in each of the plurality of 24 GHz DFS radio channels, a 24GHz radio transceiver (e.g., the primary radio 2415) to transmit thebeacon in each of the plurality of 24 GHz DFS radio channels and toreceive the radar signal in each of the plurality of 24 GHz DFS radiochannels, and a fast channel switching generator 2417 coupled to theradar detector, the beacon generator, and the 24 GHz radio transceiver.The fast channel switching generator 2417 switches the 24 GHz radio to afirst channel of the plurality of 24 GHz DFS radio channels and thencauses the beacon generator 2412 to generate the beacon in the firstchannel of the plurality of 24 GHz DFS radio channels. Then, the fastchannel switching generator 2417 causes the radar detector 2411 to scanfor the radar signal in the first channel of the plurality of 24 GHz DFSradio channels. The fast channel switching generator 2417 then repeatsthese steps for each other channel of the plurality of 24 GHz DFS radiochannels during a beacon transmission duty cycle and, in some examples,during a radar detection duty cycle. The beacon transmission duty cycleis the time between successive beacon transmissions on a given channeland the radar detection duty cycle which is the time between successivescans on a given channel. Because the DFS master 2400 cycles betweenbeaconing and scanning in each of the plurality of 24 GHz DFS radiochannels in the time window between a first beaconing and scanning in agiven channel and a subsequent beaconing and scanning the same channel,it can provide effectively simultaneous beaconing and scanning formultiple channels.

The DFS master 2400 also may contain a Bluetooth radio 2414 and/or an802.15.4 radio 2413 for communicating with other devices in the network.The DFS master 2400 may include various radio protocols 2408 tofacilitate communication via the included radio devices.

The DFS master 2400 may also include a location module 2409 to geolocateor otherwise determine the location of the DFS master 2400. In addition,the DFS master 2400 may determine the location of the DFS master 2400 byquerying the client devices 2431, 2432, which may have GPS or otherlocation-determining capabilities.

As shown in FIG. 24, the DFS master 2400 may include a scan andsignaling module 2410. The DFS master 2400 includes embedded memory2402, including for example flash storage 2401, and an embeddedprocessor 2403. The cloud agent 2404 in the DFS master 2400 facilitatesaggregation of information from the cloud agent 2404 through the cloudand includes swarm communication protocols 2405 to facilitatecommunications between DFS masters, access points, client devices, andother devices in the network. The cloud agent 2404 also includes asecurity module 2406 to protect and secure the cloud communications ofthe DFS master 2400, as well as agent protocols 2407 to facilitatecommunication with the access point control agents 2419, 2424.

The roaming and guest agents manager 2438 in the cloud intelligenceengine 2435 provides optimized connection information for devicesconnected to DFS masters that are roaming from one access point toanother access point (or from one access point to another network). Theroaming and guest agents manager 2438 also manages guest connections tonetworks for DFS masters connected to the cloud intelligence engine2435. The external data fusion engine 2439 provides for integration andfusion of information from DFS masters with information from the datasource(s) 2452. For example, the external data fusion engine 2439 mayintegrate and/or fuse information such as, but not limited to, GISinformation, other geographical information, FCC information regardingthe location of radar transmitters, FCC blacklist information, NOAAdatabases, DOD information regarding radar transmitters, DOD requests toavoid transmission in DFS channels for a given location, and/or otherinformation. The cloud intelligence engine 2435 further includes anauthentication interface 2440 for authentication of receivedcommunications and for authenticating devices and users. The radardetection compute engine 2441 aggregates radar information from the DFSmaster 2400, the DFS master(s) 2451 and/or the data source(s) 2452. Theradar detection compute engine 2441 also computes the location of radartransmitters from those data to, among other things, facilitateidentification of false positive radar detections or hidden nodes andhidden radar. The radar detection compute engine 2441 may also guide orsteer multiple DFS masters to dynamically adapt detection parametersand/or methods to further improve detection sensitivity. The locationcompute and agents manager 2442 determines the location of the DFSmaster 2400 and other connected devices (e.g., DFS master(s) 2151)through Wi-Fi lookup in a Wi-Fi location database, querying passingdevices, scan lists from DFS masters, or geometric inference.

The spectrum analysis and data fusion engine 2443 and the networkoptimization self-organization engine 2444 facilitate dynamic spectrumoptimization with information from the DFS master 2400, the other DFSmaster(s) and/or the data source(s) 2452. Each of the DFS masters (e.g.,the DFS master 2400 and/or the other DFS master(s)) connected to thecloud intelligence engine 2435 have scanned and analyzed the localspectrum and communicated that information to the cloud intelligenceengine 2435.

The DFS master 2400 sends the time-stamp signal, or dead-man switchtimer, with communications to ensure that the devices do not use theinformation, including the whitelist, beyond the useful lifetime of theinformation. For example, a whitelist will only be valid for certainperiod of time. The time-stamp signal avoids using noncompliant DFSchannels by ensuring that a device will not use the whitelist beyond itsuseful lifetime.

FIG. 25 illustrates how the cloud intelligence engine 2535 wouldinterface with client devices 2531, 2532, 2533 in a peer-to-peer network2590 and the DFS master 2500 acting as an autonomous DFS masterindependent of any access point. As shown in FIG. 25, the cloudintelligence engine 2535 may be connected to a plurality ofnetwork-connected (either directly or via network enabled devices) DFSmasters 2500, 2510. The DFS master 2500 in the peer-to-peer network 2500may connect to the cloud intelligence engine 2535 through one of thenetwork-connected client devices 2531 by, for example, piggy-backing amessage to the cloud intelligence engine 2535 on a message send to theclient devices 2531 or otherwise co-opting a connection of the clientdevices 2531 to the wide area network 2534. In the peer-to-peer network2500, the DFS master 2500 sends over-the-air control signals 2520 to theclient devices 2531, 2532, 2533 including indications of channels freeof occupying signals such as DFS channels free of radar signals.Alternatively, the DFS master communicates with just one client device2531 (e.g., a single client device 2531) which then acts as the groupowner to initiate and control the peer-to-peer communications with otherclient devices 2532, 2533. The client devices 2531, 2532, 2533 havepeer-to-peer links 2521 through which they communicate with each other.The DFS master 2500 may operate in multiple modes executing a number ofDFS scan methods employing different algorithms.

FIGS. 26 and 27 further illustrate DFS peer-to-peer networks that areenabled by the present invention. As shown in FIG. 26, a computer clientdevice 2621 may be coupled to a wide area network 2634. This couplingmay be through an access point such as an access point.

Independent of any access point, the computer client device 2621communicates via a DFS channel with a television client device 2622 andforms a peer-to-peer network 2690. The DFS master device 2600communicates with at least one of the client devices 2621, 2622 tocommunicate an available DFS channel for the peer-to-peer communication.FIG. 27 shows another exemplary peer-to-peer network. In FIG. 27, amobile client device 2731 may be coupled to a wide area network 2734.This coupling may be through an access point such as an access point.Independent of any access point, the mobile client device 2731communicates via a DFS channel with a television client device 2722 andanother mobile client device 2733 and forms a peer-to-peer network 2790with a computer client device 2721 and a wearable client device 2732. Asillustrated in FIG. 27, each of the client devices may communicate withone or more of the other client devices in the peer-to-peer network2790. The DFS master device 2700 communicates with at least one of theclient devices 2721, 2722, 2731, 2732, 2733 to communicate an availableDFS channel for the peer-to-peer communication.

In view of the subject matter described herein, methods that can beimplemented in accordance with the subject disclosure will be betterappreciated with reference to the flowcharts of FIGS. 28-29 and withfurther reference to FIGS. 33-43. While for purposes of simplicity ofexplanation, the methods are shown and described as a series of blocks,it is to be understood and appreciated that such illustrations orcorresponding descriptions are not limited by the order of the blocks,as some blocks may occur in different orders and/or concurrently withother blocks from what is depicted and described herein. Wherenon-sequential, or branched, flow illustrated via a flowchart, it can beunderstood to indicate that various other branches, flow paths, andorders of the blocks, can be implemented which achieve the same or asimilar result. Moreover, not all illustrated blocks may be required toimplement the methods described hereinafter. Additionally, it should befurther understood that the methods and/or functionality disclosedhereinafter and throughout this specification are capable of beingstored on an article of manufacture to facilitate transporting andtransferring such methods to computers, for example, as furtherdescribed herein. The terms computer readable medium, article ofmanufacture, and the like, as used herein, are intended to encompass acomputer program accessible from any computer-readable device or mediasuch as a tangible computer readable storage medium.

FIG. 28 illustrates an exemplary method 2800 according to the presentinvention for providing DFS spectrum access in peer-to-peer wirelessnetworks. First, at 2801 the standalone multi-channel DFS mastergenerates spectral information associated with a plurality of 24 GHz DFSradio channels for the standalone multi-channel DFS master. The spectralinformation may include information such as, for example, a whitelist(e.g., a whitelist of each of the plurality of 24 GHz DFS communicationchannels that does not contain a radar signal), a blacklist (e.g., ablacklist of each of the plurality of 24 GHz DFS communication channelsthat contains a radar signal), scan information associated with a scanfor a radar signal in the plurality of 24 GHz DFS communicationchannels, state information, location information associated with theDFS master device and/or client devices, time signals, scan lists (e.g.,scan lists showing neighboring access points, etc.), congestioninformation (e.g., number of re-try packets, type of re-try packets,etc.), traffic information and/or other spectral information. Next, at2802 the standalone multi-channel DFS master transmits the spectralinformation to a cloud intelligence engine via a first client device.The first client device is a network enabled device such as a cellulardevice that can connect to a wide area network and provide thatconnection to the standalone multi-channel DFS master. The cloudintelligence engine may also receive spectral information associatedwith a plurality of 24 GHz DFS communication channels from a pluralityof multi-channel DFS masters via one or more network devices.Optionally, receiving the spectral information includes receiving scaninformation associated with scanning for a radar signal in the pluralityof 24 GHz DFS radio channels. Analysis of the plurality of 24 GHz DFScommunication channels may include switching a 24 GHz radio transceiverof the DFS master device to a channel of the plurality of 24 GHz DFScommunication channels, generating a beacon in the channel of theplurality of 24 GHz DFS communication channels, and scanning for a radarsignal in the channel of the plurality of 24 GHz DFS communicationchannels.

Next, at 2803 the method of FIG. 28 includes the cloud intelligenceengine generating integrated spectral information by integrating thespectral information with other spectral information. The other spectralinformation may generated by at least one other DFS master device. Inone example, the spectral information may be integrated with the otherspectral information via one or more data fusion processes. Then, at2804 the cloud intelligence engine determines a set of available DFSradio channels for the multi-channel DFS master from the plurality of 24DFS GHz radio channels based at least on the integrated spectralinformation. For example, a communication channel may be selected fromthe plurality of 24 GHz DFS communication channels based at least on theintegrated spectral information. In an aspect, regulatory informationassociated with the plurality of 24 GHz DFS communication channelsand/or stored in at least one database may be received by the cloudintelligence engine. Furthermore, the communication channel may befurther determined based on the regulatory information. In anotheraspect, an indication of the communication channel may be provided tothe DFS master device and/or the client device(s).

FIG. 29 illustrates an exemplary method 2900 according to the presentinvention for providing DFS spectrum access in peer-to-peer wirelessnetworks. The method illustrated in FIG. 29 includes the steps describedin relation to FIG. 28 above but also includes the following optionaladditional steps. At 2910, the method includes the first client devicereceiving the available DFS radio channel and initiating communicationwith a second client device using the available DFS radio channel. Inthis step, the first client device initiates a peer-to-peercommunication network using the available DFS radio channel. At 2920,the method includes the standalone multi-channel DFS master transmittinga beacon to the first client device indicating the available DFS radiochannel. At 2930, the method includes transmitting a whitelist of eachof the plurality of 24 GHz DFS radio channels that does not contain aradar signal to the cloud intelligence engine via the first clientdevice and transmitting a blacklist of each of the plurality of 24 GHzDFS radio channels that contains a radar signal to the cloudintelligence engine via the first client device. At 2940, the methodincludes the cloud intelligence engine receiving regulatory informationstored in at least one database. And at 2950, the step of determiningthe DFS radio channel includes determining which DFS radio channel touse based on the integrated spectral information and the regulatoryinformation.

As described above, in addition to traditional infrastructure networktopologies (e.g., host Access point and clients and peer-to-peernetworks or Wi-Fi-Direct), the present inventions apply to extendedinfrastructure network topologies (e.g., mesh networks). For example,the host access points discussed herein could be a mesh peerparticipating in a mesh network and simultaneously providinginfrastructure connectivity. For example, whereas peer-to-peer networksare described herein as comprising client devices in communication witheach other and, in non-limiting aspects, in communication through anaccess point to a wide area network, wireless mesh network can comprisea communications network made up of radio nodes organized in a meshconfiguration. For instance, exemplary wireless mesh networks, asdescribed herein, can comprise mesh clients, routers, access points,and/or gateways. In a non-limiting aspect, wireless mesh clients cancomprise any of a variety of wireless devices, as described herein, inreference to peer-to-peer networks, for example, while routers and/oraccess points can forward wireless mesh client and/or othercommunications to gateways which can be connected to one or more widearea networks, such as the Internet. Accordingly, FIGS. 30-45, forexample, depict various systems, devices, and methods for reducing falsedetections and/or network downtime in exemplary mesh networks employingDFS channels, as described herein.

For instance, FIG. 30 depicts an exemplary functional block diagram of amesh network 3000, according to various non-limiting aspects asdescribed herein. In non-limiting aspects, exemplary mesh network 3000can comprise any number of host devices 3002 (e.g., router, accesspoint, etc., configured as a DFS master (e.g., a multi-channel DFSmaster), as described herein, for example, regarding FIGS. 13, 24, etc.)in communication with each other in a mesh network configurationemploying DFS channels. In conventional implementations of DFS, when apotential radar event is detected on a DFS channel, a devicecommunicating on a DFS channel has to vacate a DFS channel within 200milliseconds (ms) and stay off the DFS channel for 30 minutes. It isnoted that, while a DFS channel must be vacated within 10 seconds aftera radar detection event (e.g., a valid radar event), the 200 ms limit isderived from a test specification that limits aggregate datatransmission data over the 10 seconds with a 17 percent channel load. Asa result, as described herein, the device formerly communicating on theDFS channel cannot use the DFS channel after the 10 second specificationand until the 30 minute duration has expired, whereupon the device isrequired to monitor the DFS channel for a radar signal for 1 minuteprior to renewing communications on the DFS channel. Conventionally, aDFS master device is an access point with only one radio and is able toprovide DFS master services for just a single channel. As describedabove a significant problem of this approach is, in the event of a radarevent or a more common false detect, the single channel must be vacatedand the ability to use the DFS channel is lost.

However, as depicted in FIG. 30, for host devices 3002 (e.g., router,access point, etc., configured as a DFS master (e.g., a multi-channelDFS master), as described herein), communicating in the presence ofvarious sources of interference (e.g., random noise 3004, adjacentchannel leakages and/or interference from other channels, etc.), hostdevices 3002 can receive and/or detect 3006 such sources of interference(e.g., random noise 3004, adjacent channel leakages and/or interferencefrom other channels, etc.), which are not valid radar events, but whichcan be misdiagnosed as valid radar events (e.g., a false radar detectionor false detect), resulting in the loss DFS channel for mesh networkcommunication, unnecessarily. FIG. 30 further depicts exemplary meshnetwork 3000 comprising any number of host devices 3002 (e.g., router,access point, etc., configured as a DFS master (e.g., a multi-channelDFS master), as described herein, for example, regarding FIGS. 13, 24,etc.) that can be configured for radar information propagation 3008,according to various aspects described herein. As used herein, randomnoise 3004 refers to any source of interference that can be misdiagnosedas a valid radar event, for example, at exemplary host device 3002, orotherwise, including, but not limited to, noise, random, or otherwise,adjacent channel interference or leakages, fleeting, diminishing, weakand/or transient radar pulses, and so on.

FIG. 31 depicts another exemplary functional block diagram of a meshnetwork 3100, according to further non-limiting aspects as describedherein. It is noted, that while the central mesh node host device 3002is depicted as receiving 3006 random noise 3004 in FIG. 30, the otherexemplary mesh node host devices 3002 are not subjected to random noise3004. Furthermore, FIG. 31 depicts exemplary mesh network 3100 subjectedto radar 3102 (e.g., an actual radar source, capable of resulting in adetected valid radar event). While three of five of the exemplary meshnode host devices 3002 are depicted as receiving 3104 radar 3102 inexemplary mesh network 3100, the other exemplary mesh node host devices3002 are not. For instance, exemplary mesh node host devices 3002 can behidden, obstructed from radar, and/or subjected to differinginterference, as described above, regarding, FIGS. 19A-19C, whichillustrate the hidden node or hidden radar problem (e.g., where a nodeor radar is hidden from view by topography, obstruction, distance orchannel conditions, etc.). Accordingly, in various non-limitingembodiments of the disclosed systems, methods, and devices, redundantand/or conflicting information, as well as related information (e.g.,location information, etc.) associated with the exemplary mesh networks,exemplary mesh network node devices, and so on, can be propagated and/oremployed to facilitate reducing false detections and/or network downtimein exemplary mesh networks employing DFS channels, as described herein.Thus, various embodiments as described herein can comprise systems,methods, and devices that can employ inference and/or algorithms todiscriminate between random noise 3004 and radar 3102 to facilitatereducing false detections and/or network downtime in exemplary meshnetworks employing DFS channels, as described herein.

FIG. 32 depicts other exemplary functional block diagrams of meshnetworks 3200, according to still further non-limiting aspects asdescribed herein. For example, FIG. 32 depicts a set of exemplary meshnetwork node host devices 3202, one or more comprising a host device3206, configured as a DFS master (e.g., a primary DFS master such as amulti-channel DFS master), and an embedded agility agent 3208,configured as a DFS master (e.g., a secondary DFS master such as amulti-channel DFS master), as described herein, for example, regardingFIGS. 2, 13, 24, etc. FIG. 32 further depicts an exemplary mesh networknode host device 3206, configured as a DFS master (e.g., a primary DFSmaster), and a set 3204 of distributed agility agents 3208, configuredas secondary DFS masters, as described herein, for example, regardingFIGS. 2, 13, 24, etc. It is noted that either employing an embeddedagility agent 3208, configured as a secondary DFS master, as describedherein, for example, regarding FIGS. 2, 13, 24, etc, or employing a setof distributed agility agents 3208, configured as a secondary DFSmaster, as described herein, for example, regarding FIGS. 2, 13, 24,etc., can both provide redundant and/or conflicting informationregarding radar/noise detections, as well as related information (e.g.,location information, channel switch information, etc.) to facilitatereducing false detections and/or network downtime in exemplary meshnetworks employing DFS channels, as described herein, whereas employinga set of distributed agility agents 3208 can also provide otherinformation (e.g., location information, channel switch information,etc.) useful for reducing false detections and/or network downtime inexemplary mesh networks employing DFS channels, whereas radarinformation propagation 3008, in the case of embedded agility agent 3208can be facilitated internally by the device communications functions. Itis noted that radar information propagation 3008 can also employembedded communications channels, wired communications channels,wireless communications, whether on a DFS channel or otherwise, out ofband communications channels, such as Bluetooth, etc., and so on. Inaddition, while FIGS. 30-32 depict various configurations of exemplarymesh networks, exemplary mesh network nodes, and combinations thereof,for the purposes of illustration and not limitation, it can beappreciated that permutations of exemplary mesh networks that can employvarious non-limiting systems, methods, devices, as described herein arevirtually without limitation.

For example, while not shown in FIGS. 30-32, exemplary mesh networks cancomprise various other devices or mesh nodes such as non-DFS-masternodes, client devices, routers, access points, gateways and so, as wellas other devices and systems such as cloud intelligence engines, etc.,or portions thereof, that can employ aspects of exemplary methods asdescribed herein or send or receive communications or othertransmissions that employ aspects of or are incident to exemplarymethods as described herein, for example regarding FIGS. 2, 13-14, 24,etc.

Accordingly, in a non-limiting aspect, exemplary mesh nodes can beconfigured to store one or more of its location, distance, proximity,etc. relative to other mesh nodes in the mesh. According to furtheraspects described herein, an exemplary mesh node can be configured todetect radar 3102 (e.g., an actual radar source, capable of resulting ina detected valid radar event), to propagate a radar event (e.g., radarinformation propagation 3008, regarding a suspected radar event, avalidated radar event, etc.) throughout the exemplary mesh network, topropagate the radar event to the cloud, and/or to propagate the radarevent to nearby devices, whether to mesh network devices or otherwise,e.g., radar information propagation. According to still further aspectsdescribed herein, exemplary mesh nodes, the cloud intelligence engine,nearby devices, etc. can be configured to cast a vote based on whetherit has also detected a similar radar event (e.g., a suspected radarevent, etc.), and can be configured to propagate the vote back to theoriginating mesh network node or device. The originating mesh networknode or device can then perform inferences or algorithms configured togenerate a determination whether the radar event detected by itself,and/or other combination of voting devices, is a valid radar event(e.g., radar 3102, from an actual radar source), based on the votingfrom other mesh nodes or device in the mesh network, inferences, andalgorithms, as described herein.

Thus, in an exemplary mesh network, if one mesh node of the exemplarymesh network detects radar, various embodiments as described herein, cancompare such information to radar information propagated from neighbormesh nodes in the mesh network. Referring again to FIGS. 30 and 31, foran exemplary mesh network 3000, 3100, where each exemplary mesh nodehost device 3002 knows its location, distance, and/or proximity, etc.relative to other exemplary mesh node host devices 3002 in the meshnetwork, if the central mesh node host device 3002 detects radar (e.g.,which may be a result receiving 3006 random noise 3004 in FIG. 30), itis also very likely at least some of the neighbor mesh nodes also detectradar. In this non-limiting example, because other exemplary mesh nodehost devices 3002 in the mesh network, do not detect radar (e.g., as aresult of not receiving being subjected to or random noise 3004 in FIG.30), it can be inferred, based on one or more of an algorithm, voting,location, distance, and/or proximity, etc. relative to other exemplarymesh node host devices 3002 in the mesh network, the central mesh nodehost device 3002, or otherwise, can determine that the signal detectedis not a valid radar event (e.g., a false radar detection or falsedetect), according to further non-limiting aspects. As a result, insteadof needlessly vacating the DFS channel based on the central mesh nodehost device 3002 detecting a suspected radar event (e.g., which may be aresult receiving 3006 random noise 3004 in FIG. 30), variousembodiments, as described herein, can determine whether such a suspectedradar event is a valid radar event, such as from radar 3102 (e.g., anactual radar source, capable of resulting in a detected valid radarevent), based on radar information propagation 3008, among exemplaryneighbor mesh nodes in the exemplary mesh network, and/or otherinformation, inferences, and or algorithms.

In a further non-limiting example, exemplary embodiments can comprise anexemplary device configured for DFS detection, as described herein. Forinstance, as described above, a standalone autonomous DFS master, oragility agent can be incorporated into another device such as an accesspoint, LTE-U host, base station, cell, or small cell, media or contentstreamer, speaker, television, mobile phone, mobile router, softwareaccess point device, or peer to peer device, without limitation. Upondetection of a suspected radar event, instead of vacating all DFSchannels and only listening, suitably configured exemplary devices canbe directed, e.g., via agility agents, DFS masters, etc., to focus onlistening on the same DFS channel having the suspected radar event, tocreate redundant radar event information, in another non-limitingaspect. Accordingly, in a further non-limiting aspect, such redundantradar event information can be propagated throughout the network, e.g.,an exemplary mesh network, to facilitate further reducing instances offalse radar detection, and resultant network downtime associated withfalse detections.

In yet another non-limiting example, assuming a plurality of devices ona network employing DFS detection, as described herein, variousembodiments can employ inference and algorithms employing this redundantradar event information, to facilitate further reducing instances offalse radar detection. For instance, in an exemplary mesh network offive nodes, where each node comprises an exemplary DFS detector, forexample, as described above, regarding FIGS. 30-31, if the central meshnode host device 3002 detects radar (e.g., which may be a resultreceiving 3104 radar 3102 in FIG. 31), it is also very likely at leastsome of the neighbor mesh node host devices 3002 also detect radar. Ifone or more of the other four neighbor mesh node host devices 3002 alsodetect radar senses a radar event as well, various non-limitingembodiments can infer that the radar event sensed by the central meshnode host device 3002 is a valid radar event. Accordingly, by exploitingknowledge of location, distance, and proximity, and so on relative toother mesh nodes in the exemplary mesh network, various embodiments asdescribed herein can further ensure that spurious interference (e.g.,noise, etc) on the DFS channel is not mistaken for a real radar signal,thus further reducing the probability of false detections and/orresultant network downtime.

In a non-limiting aspect, exemplary embodiments as described herein canbe configured to one or more of perform radar information propagation3008 (e.g., including transmitting a call for validation from neighbormesh nodes of a detected radar event), to perform voting (e.g., receivea call for validation of a neighbor mesh node detected radar event, makea determination as to its own radar detection and/or vote status, andtransmit its vote), and to receive vote results prompted by the neighbormesh node detected radar event. In another non-limiting aspect, radarinformation propagation 3008 can be undertaken by employing one or moreaction frames (subject to the 200 ms limit) and/or using encodedbeacons, for example, as further described herein. In yet anothernon-limiting aspect, voting can be undertaken by employing one or moredata frames subject to the 200 ms limit after the detected radar eventand/or by employing an encoded beacon thereafter, for example, asfurther described herein.

Returning to FIG. 32, in another non-limiting aspect, one or more of theexemplary mesh nodes can be embedded with one or more agility agentsand/or radar detectors or sensors, for example, as further describedherein, regarding FIGS. 2, 20, 32, etc. To reduce the likelihood offalse detects, the exemplary mesh nodes (e.g., a host device 3206,configured as a DFS master, and an embedded agility agent 3208,configured as a DFS master, etc.) can employ multiple radar detectors orsensors (e.g., a primary DFS master such as a multi-channel DFS master,a secondary DFS master such as a multi-channel DFS master) to validate adetected radar event. In another non-limiting aspect, the one or moreagility agents and/or radar detectors or sensors, whether embedded ordistributed, can have differing priorities (e.g., primary DFS master,secondary DFS master, etc.), and/or characteristics and the finaldecision of whether the detected radar event is valid can be determinedby the data fusion of the multiple agility agents and/or radar detectorsor sensors, for example, as described herein regarding FIGS. 2, 14A,14B, etc. As a non-limiting example, an exemplary mesh node can includetwo radar detectors or sensors, for example, as further describedherein, regarding FIGS. 2, 20, 32, etc., such as a sensor on the primaryradio 215 interface, and another sensor on a dedicated radar sensingdevice, such as an embedded agility agent 3208, configured as a DFSmaster, a standalone agility agent and/or radar detector or sensor ofFIG. 20, etc. An exemplary inference for validating a detected radarevent can comprise concluding the detected radar event is valid in sucha case if both sensors detected the same radar event, where it can bepresumed that the probability of the detected radar event being a realradar (e.g., radar 2102) event is high. Another exemplary inference forvalidating a detected radar event can comprise basing the conclusionthat the detected radar event is valid on a probability (e.g., two ofthree radar sensing mesh nodes detecting, three of five radar sensingmesh nodes detecting, two of ten radar sensing mesh nodes in ageographically dispersed radar sensing mesh network detecting, etc.being above a predetermined threshold, etc.). In alternativenon-limiting aspects, another predetermined threshold can be employed,wherein if the number of radar sensing mesh nodes detecting a radarevent is below the predetermined threshold, exemplary mesh nodes canmake a determination that a longer period of time is to be employed todetermine whether more radar signals can be detected. According to afurther non-limiting aspect, an exemplary mesh node can further make adetermination to and temporarily suspend its transmitter to facilitatefocusing on radar detection and gathering further radar signals.

FIG. 33 depicts exemplary methods 3300 for reducing false detectionsand/or network downtime in exemplary mesh networks employing DFSchannels, according to various non-limiting aspects. FIG. 33 depictsportions of an exemplary mesh network 3200 comprising an exemplary meshnetwork node host device 3206, configured as a DFS master (e.g., aprimary DFS master), and a set 3204 of distributed agility agents 3208,configured as secondary DFS masters, as described herein, for example,regarding FIGS. 2, 13, 14, 24, etc. FIG. 33 further depicts exemplarymesh network 3200 in the presence of a radar 3102 burst, comprising aseries of radar pulses (e.g., radar pulses of a pulse repetitioninterval (PRI)). In a non-limiting aspect, FIG. 33 depicts exemplarymesh network node host device 3206, configured as a DFS master (e.g., aprimary DFS master) detecting a first radar pulse 3302 in the series ofradar pulses of radar 3102 burst. As further described herein, exemplarymesh network node host device 3206, configured as a DFS master (e.g., aprimary DFS master) can make an independent determination that it hasdetected a suspected radar event, as described herein, for example,regarding FIGS. 2, 13, 14, 24, etc. Subsequently, FIG. 33 depicts afirst one of the set 3204 of distributed agility agents 3208, configuredas a secondary DFS master, detecting a third radar pulse 3302 in theseries of radar pulses of radar 3102 burst, whereupon the first one ofthe set 3204 of distributed agility agents 3208 can undertake radarinformation propagation 3008, as further described herein. It is notedthat radar information propagation 3008 can be undertaken in response toa call from exemplary mesh network node host device 3206, configured asa DFS master (e.g., a primary DFS master) based on its own detection ofa suspected radar event and signaling thereof or based on the first oneof the set 3204 of distributed agility agents 3208, configured as asecondary DFS master, detecting the third radar pulse 3302, as describedherein, for example, regarding FIGS. 2, 13, 14, 24, etc. FIG. 33 depictsa second one of the set 3204 of distributed agility agents 3208,configured as a secondary DFS master, detecting a sixth radar pulse 3302in the series of radar pulses of radar 3102 burst, whereupon the secondone of the set 3204 of distributed agility agents 3208 can alsoundertake radar information propagation 3008, as further describedherein. In various non-limiting embodiments, the suspected radar eventsdetected by exemplary mesh network node host device 3206, configured asa DFS master (e.g., a primary DFS master), the first one of the set 3204of distributed agility agents 3208 can undertake radar informationpropagation 3008, and the second one of the set 3204 of distributedagility agents 3208, configured as a secondary DFS master can bevalidated according to inferences, algorithms, voting, and/or datafusion, etc., as further described herein, for example, regarding FIGS.2, 13, 14, 24, 30-32, etc. It is noted that, as depicted in FIG. 33, anexemplary inference for validating a detected radar event comprisesbasing the conclusion that the detected radar event is valid on aprobability exceeding a threshold (e.g., three of three radar sensingmesh nodes detecting suspected radar events within a predeterminedperiod of time, such as a low numbered multiple of PRI after a firstdetection of a suspected radar event, etc.).

Accordingly, at 3304, FIG. 33 depicts exemplary methods 3300 comprisinga host device (e.g., exemplary mesh network node host device 3206,configured as a DFS master, such as a primary DFS master) of exemplarymesh network 3200 operating on channel X (e.g., communicating on andsensing of a DFS channel), as further described herein, for example,regarding FIGS. 2, 13, 14, 24, 30-32, etc. At 3306, exemplary methods3300 can comprise any DFS master (e.g., a primary DFS master, asecondary DFS master, a multi-channel DFS master, a standalone DFSmaster, an agility agent 3208, whether embedded or distributed, etc.)detecting a suspected radar event, as further described herein. Incontrast to FIG. 33, FIG. 34 depicts a first one of the set 3204 ofdistributed agility agents 3208 can undertake radar informationpropagation 3008 after first detecting a suspected radar event. Inaddition, exemplary methods 3300 can further comprise collecting radarinformation from multiple DFS masters (e.g., primary DFS masters,secondary DFS masters, multi-channel DFS masters, standalone DFSmasters, agility agents 3208, whether embedded or distributed, etc.) at3308, as further described herein. As described herein, radarinformation propagation 3008 can be undertaken in response to a callfrom exemplary mesh network node host device 3206, configured as a DFSmaster (e.g., a primary DFS master) based on its own detection of asuspected radar event and signaling thereof or based on the first one ofthe set 3204 of distributed agility agents 3208, configured as asecondary DFS master, detecting the third radar pulse 3302, and so on asdescribed herein, for example, regarding FIGS. 2, 13, 14, 24, etc., andcan be collected between exemplary mesh network 3200 mesh nodes, whetheremploying a cloud intelligence engine, or otherwise. Exemplary methods3300 can further comprise, at 3310, processing the collected radarinformation to facilitate making a determination about the validity ofone or more detected radar events, or lack thereof, among exemplary meshnetwork 3200 mesh nodes. As depicted in FIG. 33, an exemplary inferencethat validates the one or more detected radar events results in thedetermination that the detected radar event is valid, at 3312, can bebased on a probability exceeding a threshold (e.g., three of three radarsensing mesh nodes detecting suspected radar events within apredetermined period of time, such as a low numbered multiple of PRIafter a first detection of a suspected radar event, etc.).

FIG. 34 depicts further non-limiting aspects of exemplary methods 330for reducing false detections and/or network downtime in exemplary meshnetworks employing DFS channels. It can be understood that thenon-limiting embodiments of FIG. 34 can represent a case where exemplarymesh network node host device 3206, configured as a DFS master (e.g., aprimary DFS master) can be hidden, obstructed from radar 3102, and/orsubjected to differing interference, as described above, regarding, FIG.31, and FIGS. 19A-19C, which illustrate the hidden node or hidden radarproblem (e.g., where a node or radar is hidden from view by topography,obstruction, distance or channel conditions, etc.). As with FIG. 33,FIG. 34 depicts portions of an exemplary mesh network 3200 comprising anexemplary mesh network node host device 3206, configured as a DFS master(e.g., a primary DFS master), and a set 3204 of distributed agilityagents 3208, configured as secondary DFS masters, as described herein,for example, regarding FIGS. 2, 13, 14, 24, etc. FIG. 34 further depictsexemplary mesh network 3200 in the presence of a radar 3102 burst,comprising a series of radar pulses (e.g., radar pulses of a PRI). In anon-limiting aspect, FIG. 34 depicts exemplary mesh network node hostdevice 3206, configured as a DFS master (e.g., a primary DFS master)failing to detect any radar pulse 3302 in the series of radar pulses ofradar 3102 burst. Concurrently, FIG. 34 depicts a first one of the set3204 of distributed agility agents 3208, configured as a secondary DFSmaster, detecting a third radar pulse 3302 in the series of radar pulsesof radar 3102 burst, whereupon the first one of the set 3204 ofdistributed agility agents 3208 can undertake radar informationpropagation 3008, as further described herein. As described above, radarinformation propagation 3008 can be undertaken based on the first one ofthe set 3204 of distributed agility agents 3208, configured as asecondary DFS master, detecting the third radar pulse 3302, as describedherein, for example, regarding FIGS. 2, 13, 14, 24, etc. FIG. 34 alsodepicts a second one of the set 3204 of distributed agility agents 3208,configured as a secondary DFS master, detecting a sixth radar pulse 3302in the series of radar pulses of radar 3102 burst, whereupon the secondone of the set 3204 of distributed agility agents 3208 can alsoundertake radar information propagation 3008, as further describedherein. In various non-limiting embodiments, the suspected radar eventsdetected by the first one of the set 3204 of distributed agility agents3208 can undertake radar information propagation 3008 and the second oneof the set 3204 of distributed agility agents 3208, configured as asecondary DFS master, can be validated according to inferences,algorithms, voting, and/or data fusion, etc., as further describedherein, for example, regarding FIGS. 2, 13, 14, 24, 30-32, etc. It isnoted that, as depicted in FIG. 34, an exemplary inference forvalidating a detected radar event comprises basing the conclusion thatthe detected radar event is valid on a probability (e.g., two of threeradar sensing mesh nodes detecting suspected radar events within apredetermined period of time, such as a low numbered multiple of PRIafter a first detection of a suspected radar event, etc.).

Accordingly, at 3304, FIG. 34 depicts exemplary methods 3300 comprisinga host device (e.g., exemplary mesh network node host device 3206,configured as a DFS master, such as a primary DFS master) of exemplarymesh network 3200 operating on channel X (e.g., communicating on andsensing of a DFS channel), as further described herein, for example,regarding FIGS. 2, 13, 14, 24, 30-32, etc. At 3306, exemplary methods3300 can comprise any DFS master (e.g., a primary DFS master, asecondary DFS master, a multi-channel DFS master, a standalone DFSmaster, an agility agent 3208, whether embedded or distributed, etc.)detecting a suspected radar event, as further described herein. Inaddition, exemplary methods 3300 can further comprise collecting radarinformation from multiple DFS masters (e.g., primary DFS masters,secondary DFS masters, multi-channel DFS masters, standalone DFSmasters, agility agents 3208, whether embedded or distributed, etc.) at3308, as further described herein. As described herein, radarinformation propagation 3008 can be undertaken based on the first one ofthe set 3204 of distributed agility agents 3208, configured as asecondary DFS master, detecting the third radar pulse 3302, and so on asdescribed herein, for example, regarding FIGS. 2, 13, 14, 24, etc., andcan be collected between exemplary mesh network 3200 mesh nodes, whetheremploying a cloud intelligence engine, or otherwise. Exemplary methods3300 can further comprise, at 3310, processing the collected radarinformation to facilitate making a determination about the validity ofone or more detected radar events, or lack thereof, among exemplary meshnetwork 3200 mesh nodes. As depicted in FIG. 34, an exemplary inferencethat validates the one or more detected radar events results in thedetermination that the detected radar event is valid, at 3312, can bebased on a probability exceeding a threshold (e.g., two of three radarsensing mesh nodes detecting suspected radar events within apredetermined period of time, such as a low numbered multiple of PRIafter a first detection of a suspected radar event, etc.).

FIG. 35 depicts other exemplary methods 3500 for reducing falsedetections and/or network downtime in exemplary mesh networks employingDFS channels, according to various non-limiting aspects. FIG. 35 depictsportions of an exemplary mesh network 3200 comprising an exemplary meshnetwork node host device 3206, configured as a DFS master (e.g., aprimary DFS master), and a set 3204 of distributed agility agents 3208,configured as secondary DFS masters, as described herein, for example,regarding FIGS. 2, 13, 14, 24, etc. FIG. 35 further depicts exemplarymesh network 3200 in the presence of sources of interference (e.g.,random noise 3004, adjacent channel leakages and/or interference fromother channels, etc.), which are not valid radar events, but which canbe misdiagnosed as valid radar events (e.g., a false radar detection orfalse detect). As described above, random noise 3004 refers to anysource of interference that can be misdiagnosed as a valid radar event,for example, at exemplary mesh network node host device 3206, orotherwise, including, but not limited to, noise, random, or otherwise,adjacent channel interference or leakages, fleeting, diminishing, weakand/or transient radar pulses, and so on. In a non-limiting aspect, FIG.35 depicts exemplary mesh network node host device 3206, configured as aDFS master (e.g., a primary DFS master) detecting a first pulse whichcould appear as a radar pulse 3302 in a series of radar pulses a seriesof radar pulses (e.g., radar pulses of a PRI) of a radar 3102 burst,which as depicted in FIG. 35 is not present in the context of exemplarymesh network 3200. As further described herein, exemplary mesh networknode host device 3206, configured as a DFS master (e.g., a primary DFSmaster) can make an independent determination that it has detected asuspected radar event, as described herein, for example, regarding FIGS.2, 13, 14, 24, etc. Subsequently, FIG. 35 depicts a first one and asecond one of the set 3204 of distributed agility agents 3208,configured as secondary DFS masters, not detecting any subsequentexpected radar pulse 3302 in a series of radar pulses (e.g., radarpulses of a radar 3102 burst, which is not present in the context ofexemplary mesh network 3200), whereupon the first one and the second oneof the set 3204 of distributed agility agents 3208 does not undertakeradar information propagation 3008, as further described herein. It isnoted that lack of radar information propagation 3008 by one or more ofthe first one or the second one of the set 3204 of distributed agilityagents 3208, configured as secondary DFS masters, as described herein,for example, regarding FIGS. 2, 13, 14, 24, etc., can be taken as anindication that the detected first pulse, which could appear as a radarpulse 3302 in a series of radar pulses a series of radar pulses (e.g.,radar pulses of a PRI) of a radar 3102 burst, detected by exemplary meshnetwork node host device 3206, configured as a DFS master (e.g., aprimary DFS master), independently or along with independent analysis ofexemplary mesh network node host device 3206, configured as a DFS master(e.g., a primary DFS master), is an invalid detected radar event. In anon-limiting aspect, this determination of invalidity of the detectedfirst pulse can be undertaken passively (e.g., by waiting for and notinglack of radar information propagation 3008 from one or more of the firstone or the second one of the set 3204 of distributed agility agents3208, configured as secondary DFS masters, on independent analysis ofthe detected first pulse, etc.) or actively (e.g., where radarinformation propagation 3008 can be undertaken in response to a callfrom exemplary mesh network node host device 3206, configured as a DFSmaster (e.g., a primary DFS master), where radar information propagation3008 comprises a negative acknowledgement of the lack of radar on a DFSchannel, etc.). In various non-limiting embodiments, the suspected radarevent (e.g., the first detected pulse) detected by exemplary meshnetwork node host device 3206, configured as a DFS master (e.g., aprimary DFS master) can be validated according to inferences,algorithms, voting, and/or data fusion, etc., as further describedherein, for example, regarding FIGS. 2, 13, 14, 24, 30-32, etc. It isnoted that, as depicted in FIG. 35, an exemplary inference forinvalidating a detected radar event (e.g., the first detected pulse)comprises basing the conclusion that the detected radar event is invalidon a probability being below a threshold (e.g., one of three radarsensing mesh nodes detecting suspected radar events within apredetermined period of time, such as a low numbered multiple of PRIafter a first detection of a suspected radar event, etc.).

Accordingly, at 3502, FIG. 35 depicts exemplary methods 3500 comprisinga host device (e.g., exemplary mesh network node host device 3206,configured as a DFS master, such as a primary DFS master) of exemplarymesh network 3200 operating on channel X (e.g., communicating on andsensing of a DFS channel), as further described herein, for example,regarding FIGS. 2, 13, 14, 24, 30-32, etc. At 3504, exemplary methods3500 can comprise any DFS master (e.g., a primary DFS master, asecondary DFS master, a multi-channel DFS master, a standalone DFSmaster, an agility agent 3208, whether embedded or distributed, etc.)detecting a suspected radar event, as further described herein. Inaddition, exemplary methods 3500 can further comprise collecting radarinformation from multiple DFS masters (e.g., primary DFS masters,secondary DFS masters, multi-channel DFS masters, standalone DFSmasters, agility agents 3208, whether embedded or distributed, etc.) at3506, as further described herein. As described herein, radarinformation propagation 3008 can be undertaken in response to a callfrom exemplary mesh network node host device 3206, configured as a DFSmaster (e.g., a primary DFS master) based on its own detection of asuspected radar event (e.g., the first detected pulse) and signalingthereof or based on another one of the first one or the second one ofthe set 3204 of distributed agility agents 3208, configured as asecondary DFS master, detecting a suspected radar event (e.g., the firstdetected pulse), and so on as described herein, for example, regardingFIGS. 2, 13, 14, 24, etc., and can be collected between exemplary meshnetwork 3200 mesh nodes, whether with a cloud intelligence engine, orotherwise. It is further noted that lack radar information propagation3008 by other exemplary mesh network 3200 mesh nodes during a time frameof interest can be employed in an inference that no other of exemplarymesh network 3200 mesh nodes experienced a corroborating suspected radarevent (e.g., a detected pulse) during the time frame of interest.Exemplary methods 3500 can further comprise, at 3508, processing thecollected radar information, or lack thereof, to facilitate making adetermination about the validity of one or more detected radar events,or lack thereof, among exemplary mesh network 3200 mesh nodes. Asdepicted in FIG. 35, an exemplary inference that invalidates a detectedradar event (e.g., the first detected pulse) results in thedetermination that the detected radar event is invalid at 3510, based ona probability being below a threshold (e.g., one of three radar sensingmesh nodes detecting suspected radar events within a predeterminedperiod of time, such as a low numbered multiple of PRI after a firstdetection of a suspected radar event, etc.).

FIG. 36 depicts further non-limiting aspects of exemplary methods 3300for reducing false detections and/or network downtime in exemplary meshnetworks employing DFS channels. FIG. 36 depicts exemplary methods 3300for reducing false detections and/or network downtime in exemplary meshnetworks employing DFS channels, according to various non-limitingaspects. FIG. 36 depicts portions of an exemplary mesh network 3200comprising an exemplary mesh network node host device 3206, configuredas a DFS master (e.g., a primary DFS master), and an exemplary secondaryDFS master 3208, which can comprise an embedded or a distributed agilityagent configured as a secondary DFS master, as described herein, forexample, regarding FIGS. 2, 13, 14, 24, etc. FIG. 36 further depictsexemplary mesh network 3200 in the presence of a radar 3102 burst,comprising a series of radar pulses (e.g., radar pulses of a PRI). In anon-limiting aspect, FIG. 36 depicts exemplary secondary DFS master 3208detecting a third radar pulse 3302 in the series of radar pulses ofradar 3102 burst. As further described herein, exemplary secondary DFSmaster 3208, which can comprise an embedded or a distributed agilityagent configured as a secondary DFS master, can make an independentdetermination that it has detected a suspected radar event, as describedherein, for example, regarding FIGS. 2, 13, 14, 24, etc. Subsequently,FIG. 36 depicts a neither the exemplary mesh network node host device3206, configured as a DFS master (e.g., a primary DFS master), nor theexemplary secondary DFS master 3208, detecting a subsequent radar pulse3302 in the series of radar pulses of radar 3102 burst, the exemplarysecondary DFS master 3208 undertaking radar information propagation3008, as further described herein. It is noted that radar informationpropagation 3008 can be undertaken in response to a call from exemplarymesh network node host device 3206, configured as a DFS master (e.g., aprimary DFS master) based on its own detection of a suspected radarevent and signaling thereof (not shown) or based on the exemplarysecondary DFS master 3208, detecting the third radar pulse 3302, asdescribed herein, for example, regarding FIGS. 2, 13, 14, 24, etc. Invarious non-limiting embodiments, the suspected radar event detected byexemplary secondary DFS master 3208 can be validated according toinferences, algorithms, voting, and/or data fusion, etc., as furtherdescribed herein, for example, regarding FIGS. 2, 13, 14, 24, 30-32,etc. It is noted that, as depicted in FIG. 36, an exemplary inferencefor validating a detected radar event comprises basing the conclusionthat the detected radar event is valid on a probability exceeding athreshold (e.g., any of the radar sensing mesh nodes detecting suspectedradar events having specified radar associated characteristics, etc.).It is noted that the instance depicted in FIG. 36 can be appropriate inparticular instances of embodiments of operations of disclosedembodiments. As non-limiting examples, exemplary secondary DFS master3208 can be associated with highly sensitive and accurate radardetection in DFS channels, regulatory information available, forexample, via cloud intelligence engine may dictate a lower threshold orhigher sensitivity for the operation in DFS channels in the presence ofradar for particular geographic areas, particular times, specialcontexts, etc.

Accordingly, at 3304, FIG. 36 depicts exemplary methods 3300 comprisinga host device (e.g., exemplary mesh network node host device 3206,configured as a DFS master, such as a primary DFS master) of exemplarymesh network 3200 operating on channel X (e.g., communicating on andsensing of a DFS channel), as further described herein, for example,regarding FIGS. 2, 13, 14, 24, 30-32, etc. At 3306, exemplary methods3300 can comprise any DFS master (e.g., a primary DFS master, asecondary DFS master, a multi-channel DFS master, a standalone DFSmaster, an agility agent 3208, whether embedded or distributed, etc.)detecting a suspected radar event, as further described herein. Incontrast to FIG. 33, FIG. 36 depicts that exemplary secondary DFS master3208 can undertake radar information propagation 3008 after firstdetecting a suspected radar event. In addition, exemplary methods 3300can further comprise collecting radar information from multiple DFSmasters (e.g., primary DFS masters, secondary DFS masters, multi-channelDFS masters, standalone DFS masters, agility agents 3208, whetherembedded or distributed, etc.) at 3308, as further described herein. Asdescribed herein, radar information propagation 3008 can be undertakenin response to call from exemplary mesh network node host device 3206,configured as a DFS master (e.g., a primary DFS master) based on its owndetection of a suspected radar event and signaling thereof (not shown)or based on the exemplary secondary DFS master 3208, detecting the thirdradar pulse 3302, and so on as described herein, for example, regardingFIGS. 2, 13, 14, 24, etc., and can be collected between exemplary meshnetwork 3200 mesh nodes, whether employing a cloud intelligence engine,or otherwise. Exemplary methods 3300 can further comprise, at 3310,processing the collected radar information to facilitate making adetermination about the validity of one or more detected radar events,or lack thereof, among exemplary mesh network 3200 mesh nodes. Asdepicted in FIG. 36, an exemplary inference that validates the one ormore detected radar events results in the determination that thedetected radar event is valid, at 3312, can be based on a probabilityexceeding a threshold (e.g., any of the radar sensing mesh nodesdetecting suspected radar events having specified radar associatedcharacteristics, etc.).

FIG. 37 depicts still further non-limiting aspects of exemplary methods3500 for reducing false detections and/or network downtime in exemplarymesh networks employing DFS channels. FIG. 37 depicts portions of anexemplary mesh network 3200 comprising an exemplary mesh network nodehost device 3206, configured as a DFS master (e.g., a primary DFSmaster), and an exemplary secondary DFS master 3208, which can comprisean embedded or a distributed agility agent configured as a secondary DFSmaster, as described herein, for example, regarding FIGS. 2, 13, 14, 24,etc. FIG. 37 further depicts exemplary mesh network 3200 in the presenceof sources of interference (e.g., random noise 3004, adjacent channelleakages and/or interference from other channels, etc.), which are notvalid radar events, but which can be misdiagnosed as valid radar events(e.g., a false radar detection or false detect). As described above,random noise 3004 refers to any source of interference that can bemisdiagnosed as a valid radar event, for example, at exemplary meshnetwork node host device 3206, or otherwise, including, but not limitedto, noise, random, or otherwise, adjacent channel interference orleakages, fleeting, diminishing, weak and/or transient radar pulses, andso on. In a non-limiting aspect, FIG. 37 depicts exemplary mesh networknode host device 3206, configured as a DFS master (e.g., a primary DFSmaster) detecting a first pulse which could appear as a radar pulse 3302in a series of radar pulses a series of radar pulses (e.g., radar pulsesof a PRI) of a radar 3102 burst, which as depicted in FIG. 37 is notpresent in the context of exemplary mesh network 3200. As furtherdescribed herein, exemplary mesh network node host device 3206,configured as a DFS master (e.g., a primary DFS master) can make anindependent determination that it has detected a suspected radar event,as described herein, for example, regarding FIGS. 2, 13, 14, 24, etc.Subsequently, FIG. 37 depicts exemplary secondary DFS master 3208detecting a subsequent pulse which could appear as a radar pulse 3302 ina series of radar pulses a series of radar pulses (e.g., radar pulses ofa radar 3102 burst, which is not present in the context of exemplarymesh network 3200), whereupon the exemplary secondary DFS master 3208can undertake radar information propagation 3008, as further describedherein. It is noted that radar information propagation 3008 by exemplarysecondary DFS master 3208, as described herein, for example, regardingFIGS. 2, 13, 14, 24, etc., can include an independent preliminarydetermination or analysis of the detected pulse (e.g., voting) orassociated information, which can be taken as an indication that thedetected pulse at exemplary secondary DFS master 3208, which couldappear as a radar pulse 3302 in a series of radar pulses a series ofradar pulses (e.g., radar pulses of a PRI) of a radar 3102 burst,detected by exemplary secondary DFS master 3208, independently or alongwith independent analysis of exemplary mesh network node host device3206, configured as a DFS master (e.g., a primary DFS master), is aninvalid detected radar event. In addition, in a further non-limitingaspect, a determination of invalidity of the detected first pulse can beundertaken passively (e.g., by waiting for and noting lack of radarpulse 3302 in a series of radar pulses a series of radar pulses (e.g.,radar pulses of a PRI) of a radar 3102 burst, noting lack of radarinformation propagation 3008 (not shown) from exemplary secondary DFSmaster 3208, based on independent analysis of the detected first pulse,etc.) or actively (e.g., where radar information propagation 3008 can beundertaken in response to a call from exemplary mesh network node hostdevice 3206, configured as a DFS master (e.g., a primary DFS master),where radar information propagation 3008 comprises a negativeacknowledgement of the lack of radar on a DFS channel, etc.). In variousnon-limiting embodiments, the suspected radar event (e.g., the firstdetected pulse) detected by exemplary mesh network node host device3206, configured as a DFS master (e.g., a primary DFS master) can beinvalidated according to inferences, algorithms, voting, and/or datafusion, etc., as further described herein, for example, regarding FIGS.2, 13, 14, 24, 30-32, etc. It is noted that, as depicted in FIG. 37, anexemplary inference for invalidating a detected radar event (e.g., thefirst detected pulse) comprises basing the conclusion that the detectedradar event is invalid on detected pulse characteristics not meeting apredetermined characteristic (e.g., no subsequently detected pulseswithin a multiple of PRI after a first detection of a suspected radarevent, etc.) and/or voting.

Accordingly, at 3502, FIG. 37 depicts exemplary methods 3300 comprisinga host device (e.g., exemplary mesh network node host device 3206,configured as a DFS master, such as a primary DFS master) of exemplarymesh network 3200 operating on channel X (e.g., communicating on andsensing of a DFS channel), as further described herein, for example,regarding FIGS. 2, 13, 14, 24, 30-32, etc. At 3504, exemplary methods3500 can comprise any DFS master (e.g., a primary DFS master, asecondary DFS master, a multi-channel DFS master, a standalone DFSmaster, an agility agent 3208, whether embedded or distributed, etc.)detecting a suspected radar event, as further described herein. Inaddition, exemplary methods 3500 can further comprise collecting radarinformation from multiple DFS masters (e.g., primary DFS masters,secondary DFS masters, multi-channel DFS masters, standalone DFSmasters, agility agents 3208, whether embedded or distributed, etc.) at3506, as further described herein. As described herein, radarinformation propagation 3008 can be undertaken in response to a callfrom exemplary mesh network node host device 3206, configured as a DFSmaster (e.g., a primary DFS master) based on its own detection of asuspected radar event (e.g., the first detected pulse) and signalingthereof or based on exemplary secondary DFS master 3208 detecting asuspected radar event (e.g., a detected pulse), and so on as describedherein, for example, regarding FIGS. 2, 13, 14, 24, etc., and can becollected between exemplary mesh network 3200 mesh nodes, whether with acloud intelligence engine, or otherwise. It is further noted that lackradar information propagation 3008 by other exemplary mesh network 3200mesh nodes during a time frame of interest can be employed in aninference that no other of exemplary mesh network 3200 mesh nodesexperienced a corroborating suspected radar event (e.g., a detectedpulse) during the time frame of interest. Exemplary methods 3500 canfurther comprise, at 3508, processing the collected radar information,or lack thereof, to facilitate making a determination about the validityof one or more detected radar events, or lack thereof, among exemplarymesh network 3200 mesh nodes. As depicted in FIG. 37, an exemplaryinference that invalidates a detected radar event (e.g., the firstdetected pulse) results in the determination that the detected radarevent is invalid at 3510, based on detected pulse characteristics notmeeting a predetermined characteristic (e.g., no subsequently detectedpulses within a multiple of PRI after a first detection of a suspectedradar event, etc.) and/or voting.

As described herein, conventional DFS masters can signal devices in aDFS network (typically client devices) by transmitting a DFS beacon,which is taken as an indication that the channel is clear of radar.Although the access point can detect radar, wireless clients typicallycannot. Because of this, wireless clients must first passively scan DFSchannels to detect whether a beacon is present on that particularchannel. During a passive scan, the client device switches throughchannels and listens for a beacon transmitted at regular intervals bythe access point on an available channel. Once a beacon is detected, theclient is allowed to actively scan on that channel. Conventionally, whena DFS master detects radar in that channel, the DFS master no longertransmits the beacon, and all client devices upon not sensing the beaconwithin a prescribed time must vacate the channel immediately and remainoff that channel for 30 minutes. For clients associated with the DFSmaster network, additional information in the beacons (e.g., the channelswitch announcement) can trigger a rapid and controlled evacuation ofthe channel.

However, in an exemplary mesh network (e.g., exemplary mesh network3200) comprising multiple exemplary mesh network node host devices 3206,configured as a DFS master (e.g., a primary DFS master), and/or agilityagents 3208, configured as secondary DFS masters, awaiting DFS channelswitching based on passive scanning for beacons transmitted at regularintervals would result in unduly slow transition of the mesh networkdevices to new or available DFS channels and/or needless networkdowntime. In addition, with each of multiple conventional mesh networknode host devices, configured as a DFS master, independently discovering(e.g., detecting) radar in DFS channels (and possibly incorrectly orincompletely), the transition of the mesh network to a new DFS can beslow as each DFS master independently discovers radar, suspends itsbeacon, client devices vacate the DFS channel, move to another DFSchannel, passively scan, and so on, or unnecessary (e.g., as a result offalse detections).

As a non-limiting example, as described above, when radar is detected ina conventional DFS network, a device has to vacate a DFS channel (e.g.,subject to the 200 ms limit data transmission limit) and stay off thechannel for 30 minutes. As a result, the device cannot use the DFSchannel during that duration for anything other than its beacon (e.g.,data transmission is not allowed). In a mesh network, there are multiplemesh network nodes. As a result, it may not be possible for all nodes inthe mesh network to vacate a DFS channel within 200 ms, especially ifthe mesh network topology is large and/or complex. However, becausebeacon transmission may still be allowed on the DFS channel to bevacated for up to a specified amount of time (e.g., 10 seconds), variousnon-limiting embodiments as described herein can employ beacon signalsencoded with information to facilitate identifying to other mesh networknodes at least one of the radar event and/or next or new channel onwhich to begin transmitting.

As a result, rather than being limited to 200 ms of data transmissionwith which to communicate next channel or other information to thevarious nodes of the mesh network, exemplary embodiments as describedherein can facilitate propagating such information for up to a specifiedamount of time (e.g., 10 seconds). Accordingly, in further non-limitingembodiments, exemplary mesh nodes as described herein can be configuredto indicate one or more of a radar event (e.g., a suspected radar event,a validated detected radar event, etc.), e.g., radar informationpropagation 3008, and a next or a new channel information in its beaconso that all neighbor exemplary mesh nodes in the exemplary mesh networkcan receive indication of the radar event (e.g., a suspected radarevent, a validated detected radar event, etc.) and next or a new channelinformation. Thus, in a non-limiting aspect, exemplary mesh nodes asdescribed herein can be configured to indicate the radar event (e.g., asuspected radar event, a validated detected radar event, etc.) and thenext or a new channel information to facilitate efficiently moving themesh network to another channel when radar is detected (e.g., asuspected radar event, a validated detected radar event, etc.). In afurther non-limiting aspect, exemplary mesh nodes in the exemplary meshnetwork can be further configured to update its own beacon with the sameinformation (e.g., the radar event and next or new channel information)in response to receiving the radar event (e.g., a suspected radar event,a validated detected radar event, etc.) and next or new channelinformation from neighbor exemplary mesh node(s) beacon(s), tofacilitate rapidly propagating the information throughout the exemplarymesh network. In yet another non-limiting aspect, exemplary mesh nodesas described herein can be configured to encode a countdown tick ortimestamp inside the beacon, such that each successive beacon reducesthe countdown tick or timestamp as the information propagates across theexemplary mesh nodes. As a result, exemplary mesh nodes of the exemplarymesh network can be further configured to coordinate their channelswitch to the next or new channel based on the next or new channelinformation on the countdown tick or timestamp when the countdown tickor timestamp reaches the final value. In other non-limitingimplementations as described herein, exemplary mesh nodes as describedherein can be configured to indicate one or more of a radar event (e.g.,a suspected radar event, a validated detected radar event, etc.), e.g.,radar information propagation 3008, and a next or a new channelinformation to neighbor exemplary mesh nodes using other mechanisms suchas data, action, management frames, or using out of band mechanisms suchas another radio, Bluetooth, or via an exemplary cloud intelligenceengine.

As a non-limiting example, for a radar event detected (e.g., a suspectedradar event, a validated detected radar event, etc.) by an exemplarymesh node of the exemplary mesh network, the radar event can bepropagated by exemplary mesh nodes configured as described hereinthroughout the exemplary mesh network, via encoding such information orother information in the exemplary mesh node's beacon signal (e.g., inthe beacon signal's information element). In contrast, conventionalnode's beacon signal's information element for channel switching is usedonly for its immediately connected devices (e.g., client devices of anaccess point). For example, if conventional access point is to switch toa different DFS channel, the access point would indicate in the beaconsignal a channel switch announcement (CSA), which would communicate toconnected devices what the next or new channel is in the CSA. However,conventionally, there is no mechanism to communicate such channel switchinformation to other access points (e.g., nodes in a mesh network),except using data frames, which are subject to the 200 ms limit on datatransmission after the radar event, which can be subject to a packetstorm, where every mesh node is sending such data frames (e.g., channelswitch information propagation via data frames) to every other node inthe mesh network.

Accordingly, in various embodiments, exemplary mesh nodes as describedherein can be configured to indicate one or more of a radar event (e.g.,a suspected radar event, a validated detected radar event, etc.), e.g.,radar information propagation 3008, and a next or a new channelinformation in its beacon so that all neighbor exemplary mesh nodes inthe exemplary mesh network can receive indication of the radar event(e.g., a suspected radar event, a validated detected radar event, etc.)and next or a new channel information. As a result, for a radar event(e.g., a suspected radar event, a validated detected radar event, etc.)detected by an exemplary mesh node of an exemplary mesh network,according to various non-limiting embodiments as described herein, theradar event (e.g., a suspected radar event, a validated detected radarevent, etc.) can be propagated throughout the mesh network, via encodingsuch information or other information in the exemplary mesh node'sbeacon signal (e.g., in the beacon signal's information element), whichbeacons are synchronized for all exemplary mesh nodes participating inthe mesh network. Accordingly, each exemplary mesh node receiving theinformation encoded beacon can be configured to indicate one or more ofthe radar event (e.g., a suspected radar event, a validated detectedradar event, etc.) and the next or new channel information in its beaconso that all neighbor exemplary mesh nodes receives the radar event(e.g., a suspected radar event, a validated detected radar event, etc.)and the next or new channel information. Exemplary mesh nodes, asdescribed herein, can be configured to update its own beacon with thesame information, upon receiving the radar event (e.g., a suspectedradar event, a validated detected radar event, etc.) and the next or newchannel information from its neighbor exemplary mesh nodes' beacons.

In other disclosed embodiments, various non-limiting implementations, asdescribed herein, can be configured to employ out of band mechanismssuch as another radio, Bluetooth, LTE, etc., or via an exemplary cloudintelligence engine, for radar information propagation 3008, or topropagate other information, such as channel switch information, and canbe configured to employ other mechanisms, e.g., data, action, managementframes, to facilitate propagation radar and/or other information, forexample, as described herein regarding FIGS, 2, 21-24, 30-32, etc. As anon-limiting example, for another device that is not part of theexemplary mesh network (e.g., a peer to peer device, etc.), which may ormay not have the ability to detect radar, is operating on the same DFSchannel, various non-limiting embodiments, as described herein, canemploy out of band mechanisms such as another radio, Bluetooth, LTE,etc., or via an exemplary cloud intelligence engine, to facilitate radarinformation propagation or propagation of other information, such aschannel switch information, and can employ other mechanisms, e.g., data,action, management frames, to facilitate radar information propagation3008 and/or propagation of other information. Accordingly, variousnon-limiting embodiments as described herein, can facilitate radarinformation propagation and propagation of other information, such aschannel switch information beyond devices participating in the exemplarymesh network, e.g., peer to peer devices, etc. For example,traditionally, when an access point leaves a DFS channel, a peer to peerdevice operating on the same DFS channel must vacate the DFS channel.Without the benefit of radar information propagation 3008 and channelswitch information propagation, as described herein, the peer to peerdevice must scan the next DFS channel for one minute prior to switchingto it.

FIG. 38 depicts exemplary methods 3800 for reducing false detectionsand/or network downtime in exemplary mesh networks 3000, 3100, 3200,etc. employing DFS channels, according to various non-limiting aspects.FIG. 38 depicts portions of an exemplary mesh network 3200 comprising anexemplary mesh network node host device 3206, configured as a DFS master(e.g., a multi-channel DFS master), and a set of nearby or neighborexemplary mesh network node host devices 3206, configured as DFSmasters, as described herein, for example, regarding FIGS. 2, 13, 14,24, 30-32, etc. FIG. 38 further depicts exemplary mesh network 3200 inthe presence of a radar 3102 burst, comprising a series of radar pulses(e.g., radar pulses of a PRI). In a non-limiting aspect, FIG. 38 depictsexemplary mesh network node host devices 3206, configured as DFS masters(e.g., multi-channel DFS masters), detecting a first radar pulse 3302 inthe series of radar pulses of radar 3102 burst. As further describedherein, exemplary mesh network node host device 3206, configured as aDFS master can make an independent determination that it has detected asuspected radar event, as described herein, for example, regarding FIGS.2, 13, 14, 24, etc. Subsequently, FIG. 38 depicts a first one of the setof exemplary mesh network node host devices 3206, configured as DFSmasters (e.g., multi-channel DFS masters), detecting a third radar pulse3302 in the series of radar pulses of radar 3102 burst, whereupon thefirst one of the set of exemplary mesh network node host devices 3206,configured as DFS masters (e.g., multi-channel DFS masters), canundertake radar information propagation 3008, as further describedherein. It is noted that radar information propagation 3008 can beundertaken in response to a call from exemplary mesh network node hostdevice 3206, configured as a DFS master (e.g., a primary DFS master)based on its own detection of a suspected radar event and signalingthereof or based on the first one or the second one of the set ofexemplary mesh network node host devices 3206, configured as DFS masters(e.g., multi-channel DFS masters), detecting the third radar pulse 3302or the sixth radar pulse 3302, as described herein, for example,regarding FIGS. 2, 13, 14, 24, etc. Accordingly, FIG. 38 depicts asecond one of the set of exemplary mesh network node host devices 3206,configured as DFS masters (e.g., multi-channel DFS masters), detecting asixth radar pulse 3302 in the series of radar pulses of radar 3102burst, whereupon the second one of the set 3204 of distributed agilityagents 3208 can also undertake radar information propagation 3008, asfurther described herein. In various non-limiting embodiments, thesuspected radar events detected by exemplary mesh network node hostdevice 3206, configured as a DFS master (e.g., a multi-channel DFSmaster), the first one of the set of exemplary mesh network node hostdevices 3206, configured as DFS masters (e.g., multi-channel DFSmasters), and the second one of the set of exemplary mesh network nodehost devices 3206, configured as DFS masters (e.g., multi-channel DFSmasters) can be validated according to inferences, algorithms, voting,and/or data fusion, etc., as further described herein, for example,regarding FIGS. 2, 13, 14, 24, 30-32, etc. It is noted that, as depictedin FIG. 38, an exemplary inference for validating a detected radar eventcomprises basing the conclusion that the detected radar event is validon a probability exceeding a threshold (e.g., three of three radarsensing mesh nodes detecting suspected radar events within apredetermined period of time, such as a low numbered multiple of PRIafter a first detection of a suspected radar event, etc.) and based onselecting such radar sensing mesh nodes based on location informationassociated with the set of nearby or neighboring exemplary mesh networknode host devices 3206, configured as DFS masters.

Accordingly, at 3802, FIG. 38 depicts exemplary methods 3800 comprisinga host device (e.g., exemplary mesh network node host device 3206,configured as a DFS master, such as a multi-channel DFS master) ofexemplary mesh network 3200 operating on channel X (e.g., communicatingon and sensing of a DFS channel), as further described herein, forexample, regarding FIGS. 2, 13, 14, 24, 30-32, etc. At 3804, exemplarymethods 3800 can comprise any DFS master (e.g., a primary DFS master, asecondary DFS master, a multi-channel DFS master, a standalone DFSmaster, an agility agent 3208, whether embedded or distributed, etc.)detecting a suspected radar event, as further described herein. Inaddition, exemplary methods 3800 can further comprise collecting orreceiving radar information from multiple DFS masters (e.g., primary DFSmasters, secondary DFS masters, multi-channel DFS masters, standaloneDFS masters, agility agents 3208, whether embedded or distributed, etc.)at 3806, as further described herein. As described herein, radarinformation propagation 3008 can be undertaken in response to a callfrom exemplary mesh network node host device 3206, configured as a DFSmaster (e.g., a primary DFS master, a multi-channel DFS master, etc.)based on its own detection of a suspected radar event and signalingthereof or based on the first one or the second one of the set of nearbyor neighboring exemplary mesh network node host devices 3206, configuredas DFS masters (e.g., secondary DFS master, multi-channel DFS masters),detecting the third radar pulse 3302 or the sixth radar pulse,respectively, and so on as described herein, for example, regardingFIGS. 2, 13, 14, 24, etc., and can be collected between exemplary meshnetwork 3200 mesh nodes, whether employing a cloud intelligence engine,or otherwise. Exemplary methods 3800 can further comprise, at 3808,processing the collected radar information to facilitate making adetermination about the validity of one or more detected radar events,or lack thereof, among exemplary mesh network 3200 mesh nodes, based onlocation information associated with the set of nearby or neighboringexemplary mesh network node host devices 3206, configured as DFSmasters. As depicted in FIG. 38, an exemplary inference that validatesthe one or more detected radar events results in the determination thatthe detected radar event is valid, at 3810, can be based on aprobability exceeding a threshold (e.g., three of three radar sensingmesh nodes detecting suspected radar events within a predeterminedperiod of time, such as a low numbered multiple of PRI after a firstdetection of a suspected radar event, etc.) and based on selecting suchradar sensing mesh nodes based on location information associated withthe set of nearby or neighboring exemplary mesh network node hostdevices 3206, configured as DFS masters.

FIG. 39 depicts still further non-limiting aspects of exemplary methods3900 for reducing false detections and/or network downtime in exemplarymesh networks 3000, 3100, 3200, etc. employing DFS channels. FIG. 39depicts portions of an exemplary mesh network 3200 comprising anexemplary mesh network node host device 3206, configured as a DFS master(e.g., a multi-channel DFS master), and a set of nearby or neighborexemplary mesh network node host devices 3206, configured as DFSmasters, as described herein, for example, regarding FIGS. 2, 13, 14,24, 30-32, etc. FIG. 39 further depicts exemplary mesh network 3200 inthe presence of sources of interference (e.g., random noise 3004,adjacent channel leakages and/or interference from other channels,etc.), which are not valid radar events, but which can be misdiagnosedas valid radar events (e.g., a false radar detection or false detect).As described above, random noise 3004 refers to any source ofinterference that can be misdiagnosed as a valid radar event, forexample, at exemplary mesh network node host device 3206, or otherwise,including, but not limited to, noise, random, or otherwise, adjacentchannel interference or leakages, fleeting, diminishing, weak and/ortransient radar pulses, and so on. In a non-limiting aspect, FIG. 39depicts exemplary mesh network node host devices 3206, configured as DFSmasters (e.g., multi-channel DFS masters), detecting a first pulse whichcould appear as a radar pulse 3302 in a series of radar pulses a seriesof radar pulses (e.g., radar pulses of a PRI) of a radar 3102 burst,which as depicted in FIG. 39 is not present in the context of exemplarymesh network 3200. As further described herein, exemplary mesh networknode host device 3206, configured as a DFS master can make anindependent determination that it has detected a suspected radar event,as described herein, for example, regarding FIGS. 2, 13, 14, 24, etc.Subsequently, FIG. 39 depicts a first one and a second one of the set ofexemplary nearby or neighboring mesh network node host devices 3206,configured as DFS masters (e.g., multi-channel DFS masters), notdetecting any subsequent expected radar pulse 3302 in a series of radarpulses (e.g., radar pulses of a radar 3102 burst, which is not presentin the context of exemplary mesh network 3200), whereupon the first oneand the second one of the set of nearby or neighbor exemplary meshnetwork node host devices 3206, configured as DFS masters, does notundertake radar information propagation 3008, as further describedherein. It is noted that lack of radar information propagation 3008 byone or more of the first one or the second one of the set of nearby orneighbor exemplary mesh network node host devices 3206, configured asDFS masters, as described herein, for example, regarding FIGS. 2, 13,14, 24, etc., can be taken as an indication that the detected firstpulse, which could appear as a radar pulse 3302 in a series of radarpulses a series of radar pulses (e.g., radar pulses of a PRI) of a radar3102 burst, detected by exemplary mesh network node host device 3206,configured as a DFS master (e.g., a primary DFS master), independentlyor along with independent analysis of exemplary mesh network node hostdevice 3206, configured as a DFS master (e.g., a primary DFS master), isan invalid detected radar event. In a non-limiting aspect, thisdetermination of invalidity of the detected first pulse can beundertaken passively (e.g., by waiting for and noting lack of radarinformation propagation 3008 from one or more of the first one or thesecond one of the set of nearby or neighbor exemplary mesh network nodehost devices 3206, configured as DFS masters, on independent analysis ofthe detected first pulse, etc.) or actively (e.g., where radarinformation propagation 3008 can be undertaken in response to a callfrom exemplary mesh network node host device 3206, configured as a DFSmaster (e.g., a multi-channel DFS master), where radar informationpropagation 3008 comprises a negative acknowledgement of the lack ofradar on a DFS channel, etc.). In various non-limiting embodiments, thesuspected radar event (e.g., the first detected pulse) detected byexemplary mesh network node host device 3206, configured as a DFS master(e.g., multi-channel DFS masters) can be validated according toinferences, algorithms, voting, and/or data fusion, etc., as furtherdescribed herein, for example, regarding FIGS. 2, 13, 14, 24, 30-32,etc. It is noted that, as depicted in FIG. 39, an exemplary inferencefor invalidating a detected radar event (e.g., the first detected pulse)comprises basing the conclusion that the detected radar event is invalidon a probability being below a threshold (e.g., one of three radarsensing mesh nodes detecting suspected radar events within apredetermined period of time, such as a low numbered multiple of PRIafter a first detection of a suspected radar event, etc.) and based onselecting such radar sensing mesh nodes based on location informationassociated with the set of nearby or neighboring exemplary mesh networknode host devices 3206, configured as DFS masters.

Accordingly, at 3902, FIG. 39 depicts exemplary methods 3300 comprisinga host device (e.g., exemplary mesh network node host device 3206,configured as a DFS master, such as a multi-channel DFS master) ofexemplary mesh network 3200 operating on channel X (e.g., communicatingon and sensing of a DFS channel), as further described herein, forexample, regarding FIGS. 2, 13, 14, 24, 30-32, etc. At 3904, exemplarymethods 3900 can comprise any DFS master (e.g., a primary DFS master, asecondary DFS master, a multi-channel DFS master, a standalone DFSmaster, an agility agent 3208, whether embedded or distributed, etc.)detecting a suspected radar event, as further described herein. Inaddition, exemplary methods 3900 can further comprise collecting orreceiving radar information from multiple DFS masters (e.g., primary DFSmasters, secondary DFS masters, multi-channel DFS masters, standaloneDFS masters, agility agents 3208, whether embedded or distributed, etc.)at 3906, as further described herein. As described herein, radarinformation propagation 3008 can be undertaken in response to a callfrom exemplary mesh network node host device 3206, configured as a DFSmaster (e.g., a primary DFS master) based on its own detection of asuspected radar event (e.g., the first detected pulse, a multi-channelDFS master, etc.) and signaling thereof or based on another one of thefirst one or the second one of the set of nearby or neighbor exemplarymesh network node host devices 3206, configured as DFS masters,detecting a suspected radar event (e.g., the first detected pulse), andso on as described herein, for example, regarding FIGS. 2, 13, 14, 24,etc., and can be collected between exemplary mesh network 3200 meshnodes, whether with a cloud intelligence engine, or otherwise. It isfurther noted that lack radar information propagation 3008 by otherexemplary mesh network 3200 mesh nodes during a time frame of interestcan be employed in an inference that no other of exemplary mesh network3200 mesh nodes experienced a corroborating suspected radar event (e.g.,a detected pulse) during the time frame of interest. Exemplary methods3900 can further comprise, at 3908, processing the collected radarinformation, or lack thereof, to facilitate making a determination aboutthe validity of one or more detected radar events, or lack thereof,among exemplary mesh network 3200 mesh nodes, based on locationinformation associated with the set of nearby or neighboring exemplarymesh network node host devices 3206, configured as DFS masters. Asdepicted in FIG. 39, an exemplary inference that invalidates a detectedradar event (e.g., the first detected pulse) results in thedetermination that the detected radar event is invalid at 3910, can bebased on a probability being below a threshold (e.g., one of three radarsensing mesh nodes detecting suspected radar events within apredetermined period of time, such as a low numbered multiple of PRIafter a first detection of a suspected radar event, etc.) and based onselecting such radar sensing mesh nodes based on location informationassociated with the set of nearby or neighboring exemplary mesh networknode host devices 3206, configured as DFS masters.

Accordingly, in a non-limiting aspect, an exemplary mesh network node(e.g., exemplary mesh network node host device 3206, etc.) can beconfigured to call upon or signal its nearby or neighboring exemplarymesh network nodes to assist in the radar detection, based on theexemplary mesh network node (e.g., exemplary mesh network node hostdevice 3206, etc.) detection of a suspected radar event. As anon-limiting example, in response to receiving the call or signal toassist on the radar detection, one or more nearby or neighboringexemplary mesh network nodes can be configured to temporarily suspendits transmissions (e.g., temporarily suspend its transmissions on one ormore DFS channels, etc.), increase its receiver gain to facilitatefocusing on radar detection, and/or send out an exemplary clear-to-send(CTS) frame to silent one or more DFS channels of interest, for example,as further described herein. In addition, exemplary mesh network nodes(e.g., exemplary mesh network node host devices 3206, etc.) can befurther configured to vote on whether the radar signal, which is thesubject of the suspected radar event, is a real radar 3104, radar 3102(e.g., an actual radar source, capable of resulting in a validatedand/or detected radar event).

As a non-limiting aspect, in further non-limiting embodiments ofexemplary mesh networks 3000, 3100, 3200, etc. associated with acentrally or singularly managed or controlled network employing DFSchannels (e.g., such as an enterprise or other network comprising acontroller that controls multiple access points, other multi-nodenetworks comprising a combination of wired or wireless segments, whetherhaving central management or control, or otherwise, etc.), employing aCTS frame may not be necessary or desirable. For instance, an exemplaryradar detector (e.g., a primary DFS master, a secondary DFS master, amulti-channel DFS master, a standalone DFS master, an agility agent3208, whether embedded or distributed, etc.) that detects a suspectedradar signal can be configured to be wired or wirelessly connected tothe enterprise network and can be further configured to send a signalover the centrally or singularly managed or controlled network employingDFS channels network to ask one or more other routers/access points orany other mesh node in the mesh network (e.g., itself, and/or via anyother device associated with the mesh network, etc.) to silence, withouthaving to send out a CTS frame (e.g., via an out of band method such asvia a secondary radio like 2.4 GHz WLAN, via wired network segments, viaa direct ethernet frame, etc.), to facilitate propagation of themessage/signal to mesh nodes in exemplary mesh networks employing DFSchannels without generating a signal on the one or more DFS channels. Asa result, in a non-limiting aspect, exemplary embodiments, as describedherein, can be configured to send a packet or data that signals nearbyor neighboring devices on the same network, for which such nearby orneighboring devices can also be configured to turn off their transmitterfor a predetermined period of time, request neighbor devices on the samenetwork to assist in radar detection on the DFS channel, etc.

In a further non-limiting example, consider an exemplary routeremploying DFS detection, (e.g., comprising or being associated with aprimary DFS master, a secondary DFS master, a multi-channel DFS master,a standalone DFS master, an agility agent 3208, whether embedded ordistributed, etc.), as described herein, for example, regarding FIGS. 2,13, 14, 20, 24, 30-32, etc. In a non-limiting aspect, instead ofblocking or silencing all DFS channels and only listening in response toa suspected radar event, exemplary router having DFS detection can beconfigured to be directed, e.g., via agility agents, to facilitatefocusing on listening on the same DFS channel having the suspected radarsignal, to create redundant radar event information, and so on, asdescribed herein, for example, regarding FIGS. 2, 13, 14, 20, 24, 30-42,etc. Accordingly, in further non-limiting embodiments of the disclosedsubject matter, redundant radar event information (e.g., radarinformation propagation 3008) can be propagated throughout exemplarynetworks, to facilitate further reducing instances of false radardetection, and/or resultant network downtime.

In yet another non-limiting example, assuming a plurality of exemplarydevices on an exemplary network employing DFS detection (e.g.,comprising or being associated with a primary DFS master, a secondaryDFS master, a multi-channel DFS master, a standalone DFS master, anagility agent 3208, whether embedded or distributed, etc.), as describedherein, for example, regarding FIGS. 2, 13, 14, 20, 24, 30-32, etc.,various embodiments can employ inference and algorithms employing thisredundant radar event information, to facilitate further reducinginstances of false radar detection. For instance, as described above, inan exemplary mesh network of five mesh nodes, where each mesh nodecomprises or is associated with an exemplary DFS detector (e.g.,comprising or being associated with a primary DFS master, a secondaryDFS master, a multi-channel DFS master, a standalone DFS master, anagility agent 3208, whether embedded or distributed, etc.), if the meshcenter node DFS detector senses a suspected radar event, and the otherfour mesh node DFS detectors do not, various non-limiting embodimentscan infer that the suspected radar event sensed by the center mesh nodeDFS detector is not a valid radar event. Conversely, in an exemplarymesh network of five mesh nodes, where each mesh node comprises anexemplary DFS detector (e.g., comprising or being associated with aprimary DFS master, a secondary DFS master, a multi-channel DFS master,a standalone DFS master, an agility agent 3208, whether embedded ordistributed, etc.), if the center mesh node DFS detector senses asuspected radar event, and one or more of the other four DFS detectorssenses a suspected radar event as well, various non-limiting embodimentscan infer that the suspected radar event sensed by the center mesh nodeDFS detector is a valid radar event. Accordingly, as further describedherein, for example, regarding FIGS. 2, 13, 14, 20, 24, 30-42, etc., byexploiting knowledge of location, distance, and proximity, of other meshnodes in the exemplary mesh network, various embodiments as describedherein, can further ensure that spurious interference (e.g., noise) onthe DFS channel is not mistaken for an real radar signal, thus furtherreducing the probability of false detections and/or resultant networkdowntime.

Moreover, by employing radar information propagation 3008 (or lackthereof, by a particular exemplary mesh node) and information regardingdevice location, radar and channel availability (e.g., whitelists,blacklists, etc.), voting, accuracy, history, etc. available inexemplary networks (e.g., exemplary mesh networks), as described herein,further derivative information regarding radar detection effectiveness(e.g., node trust information) can be generated and/or employed byvarious embodiments herein, e.g., via a cloud intelligence engine, orotherwise, to facilitate employing inference and/or algorithms todiscriminate between random noise 3004 and radar 3102 to facilitatereducing false detections and/or network downtime in exemplary networksexemplary networks (e.g., exemplary mesh networks) employing DFSchannels, as described herein. As a non-limiting example, considerportions of an exemplary mesh network 3200 comprising an exemplary meshnetwork node host device 3206, configured as a DFS master (e.g., amulti-channel DFS master), and a set of nearby or neighbor exemplarymesh network node host devices 3206, configured as DFS masters, asdescribed herein, for example, regarding FIGS. 2, 13, 14, 24, 30-32,etc. As a non-limiting example, if a particular nearby or neighborexemplary mesh network node host device 3206, configured as a DFSmaster, (or other device) has a voting history (e.g., stored, analyze,etc., via a cloud intelligence engine, etc.) that is consistentlyantithetical with the results of radar information propagation 3008,voting, validating and/or invalidating suspected radar events, then, atrust metric for that exemplary mesh network node host device 3206 (orother device) can be deprecated, updated, and/or otherwise annotated,such that inferences and/or algorithms employing radar informationpropagation 3008 from that exemplary mesh network node host device 3206(or other device) can be taken into account to facilitate reducing falsedetections and/or network downtime, improving the quality of radardetection in exemplary networks employing channels and/or exemplarynetwork response to valid radar signals.

FIG. 40 depicts other exemplary methods 4000 for reducing falsedetections and/or network downtime, based on propagating radar andlocation information, in exemplary mesh networks 3000, 3100, 3200, etc.employing DFS channels, according to various non-limiting aspects.Accordingly, at 4002, FIG. 40 depicts exemplary methods 4000 comprisingan exemplary host device (e.g., exemplary mesh network node host device3206, configured as a DFS master, such as a multi-channel DFS master) ofan exemplary mesh network (e.g., exemplary mesh network 3200) operatingon channel X (e.g., communicating on and sensing of a DFS channel), asfurther described herein, for example, regarding FIGS. 2, 13, 14, 24,30-39, etc. At 4004, exemplary methods 4000 can comprise exemplary hostdevice (e.g., exemplary mesh network node host device 3206, configuredas a DFS master, such as a multi-channel DFS master) detecting asuspected radar event, as further described herein. In addition, radarinformation propagation 3008 including location information can beundertaken in response to an exemplary host device (e.g., exemplary meshnetwork node host device 3206, configured as a DFS master, such as amulti-channel DFS master) detection of the suspected radar event and/orsignaling thereof, as described herein, for example, regarding FIGS. 2,13, 14, 24, 30-39, etc., which can be distributed among exemplary meshnetwork 3200 mesh nodes, whether employing a cloud intelligence engine,or otherwise.

In another non-limiting embodiment of exemplary methods 4000 forreducing false detections and/or network downtime, based on propagatingradar and location information, in exemplary mesh networks 3000, 3100,3200, etc. employing DFS channels, exemplary methods 4000 can comprise,at 4008, receiving radar information propagation 3008, includinglocation information, at an exemplary host device (e.g., exemplary meshnetwork node host device 3206, configured as a DFS master, such as amulti-channel DFS master) of an exemplary mesh network (e.g., exemplarymesh network 3200) operating on channel X (e.g., communicating on andsensing of a DFS channel), as further described herein, for example,regarding FIGS. 2, 13, 14, 24, 30-39, etc.

In addition, exemplary methods 4000 can further comprise collecting orreceiving further radar information from one or more multiple DFSmasters (e.g., primary DFS masters, secondary DFS masters, multi-channelDFS masters, standalone DFS masters, agility agents 3208, whetherembedded or distributed, etc.) at 4010, as further described herein. Asdescribed herein, radar information propagation 3008 can be undertakenin response to a call from an exemplary host device (e.g., exemplarymesh network node host device 3206, configured as a DFS master, such asa multi-channel DFS master) detection of the suspected radar event basedon its own detection of a suspected radar event and signaling thereof orbased on one or more of a set of nearby or neighboring exemplary meshnetwork node host devices 3206, configured as DFS masters (e.g.,multi-channel DFS masters), detecting the same or a subsequent radarpulse 3302, and so on as described herein, for example, regarding FIGS.2, 13, 14, 24, etc., and can be collected between exemplary mesh network3200 mesh nodes, whether employing a cloud intelligence engine, orotherwise. Accordingly, exemplary methods 4000 can further comprise, at4010, processing the collected radar information to facilitate making adetermination about the validity of one or more detected radar events,or lack thereof, among exemplary mesh network 3200 mesh nodes, based onlocation information associated with the set of nearby or neighboringexemplary mesh network node host devices 3206, e.g., configured as DFSmasters, etc.

As described above, as depicted in FIG. 40, an exemplary inference thatinvalidates a detected radar event (e.g., a first detected pulse in anexemplary mesh network employing DFS detection, as described herein)results in the determination that the detected radar event is invalid(e.g., is Not Radar), can be based on a probability being below athreshold (e.g., one of three radar sensing mesh nodes detectingsuspected radar events within a predetermined period of time, such as alow numbered multiple of PRI after a first detection of a suspectedradar event, etc.) and based on selecting such radar sensing mesh nodesbased on location information associated with the set of nearby orneighboring exemplary mesh network node host devices 3206, configured asDFS masters. As a result, exemplary methods 4000 can further comprise,at 4012, radar information propagation 3008, including locationinformation, e.g., via one or more exemplary mesh network node hostdevice 3206, configured as a DFS master, etc., which can be undertakenas a result of invalidating the suspected radar event, e.g., via anexemplary host device (e.g., exemplary mesh network node host devices3206, configured as DFS masters, such as multi-channel DFS masters), viaa cloud intelligence engine, or otherwise.

As further described above, as depicted in FIG. 40, an exemplaryinference that validates the one or more detected radar events resultsin the determination that the detected radar event is valid (e.g., IsRadar), can be based on a probability exceeding a threshold (e.g., threeof three radar sensing mesh nodes detecting suspected radar eventswithin a predetermined period of time, such as a low numbered multipleof PRI after a first detection of a suspected radar event, etc.) andbased on selecting such radar sensing mesh nodes based on locationinformation associated with the set of nearby or neighboring exemplarymesh network node host devices 3206, configured as DFS masters. As aresult, exemplary methods 4000 can further comprise, at 4012, radarinformation propagation 3008, including location information, e.g., viaone or more exemplary mesh network node host devices 3206, configured asDFS masters, etc., which can be undertaken as a result of invalidatingthe suspected radar event, e.g., via an exemplary host device (e.g.,exemplary mesh network node host devices 3206, configured as DFSmasters, such as multi-channel DFS masters), via a cloud intelligenceengine, or otherwise. In addition, exemplary methods 4000 can furthercomprise, at 4012, initiating DFS channel change (e.g., after radarinformation propagation 3008, including location information), forexample, via an encoded beacon signal, propagated via one or moreexemplary mesh network node host devices 3206, configured as DFSmasters, via other mechanisms such as data, action, management frames,or using out of band mechanisms such as another radio, Bluetooth, and/orvia an exemplary cloud intelligence engine, etc., as further describedabove.

FIG. 41 depicts still other exemplary methods 4100 for reducing falsedetections and/or network downtime, based on employing a control,management, and/or data frame, in exemplary mesh networks 3000, 3100,3200, etc., employing DFS channels, according to various non-limitingaspects. In further non-limiting embodiments of an exemplary meshnetwork node host device 3206, configured as a DFS master (e.g., amulti-channel DFS master), a set of nearby or neighbor exemplary meshnetwork node host devices 3206, configured as DFS masters, as describedherein, for example, regarding FIGS. 2, 13, 14, 24, 30-32, etc. for anexemplary DFS master (e.g., a primary DFS master, a secondary DFSmaster, a multi-channel DFS master, a standalone DFS master, an agilityagent 3208, whether embedded or distributed, etc.) detecting a suspectedradar event (e.g., detecting a number N of radar pulses 3302 greaterthan or equal to a predetermined number X of radar pulses 3302), theexemplary DFS master can be configured to transmit an exemplary control,management, and/or data frame, e.g., an exemplary CTS frame 4102, etc.on one or more DFS channels to facilitate silencing the one or more DFSchannels. In a further non-limiting aspect, an exemplary DFS master,after it transmits exemplary control, management, and/or data frame,e.g., an exemplary CTS frame 4102, etc., on one or more DFS channels,and can be further configured to continue to sense for a radar 3102signal, as further described herein. Whereas in exemplary mesh networks3000, 3100, 3200, etc. associated with the controlled network employingDFS channels (e.g., an enterprise network comprising a controller thatcontrols multiple access points, etc.), employing a control, management,and/or data frame, e.g., an exemplary CTS frame 4102, etc., on one ormore DFS channels may not be necessary or desirable, as described above,an exemplary radar detector (e.g., a primary DFS master, a secondary DFSmaster, a multi-channel DFS master, a standalone DFS master, an agilityagent 3208, whether embedded or distributed, etc.) associated with anuncontrolled network employing DFS channels that detects a suspectedradar event (e.g., detecting a number N of radar pulses 3302 greaterthan or equal to a predetermined number X of radar pulses 3302) can alsobe configured to be wired or wireles sly connected to the network andcan be further configured to send a signal over the enterprise networkto ask one or more other routers/access points or any other mesh node inthe mesh network to silence, without having to send out a CTS frame 4102(e.g., an out of band method, a direct ethernet frame, etc.). However,in the instance of non-limiting embodiments of uncontrolled exemplarymesh networks, comprising exemplary mesh network node host device 3206,configured as a DFS master (e.g., a multi-channel DFS master), a set ofnearby or neighbor exemplary mesh network node host devices 3206,configured as DFS masters, as described herein, for example, regardingFIGS. 2, 13, 14, 24, 30-32, etc., and so on, the exemplary DFS master(e.g., a primary DFS master, a secondary DFS master, a multi-channel DFSmaster, a standalone DFS master, an agility agent 3208, whether embeddedor distributed, etc.) can be configured to transmit an exemplarycontrol, management, and/or data frame, e.g., an exemplary CTS frame4102, etc., on one or more DFS channels to facilitate silencing the oneor more DFS channels (e.g., silence transmitters on one or more DFSchannels of nearby or neighboring nodes to terminate or to holdtransmissions on the one or more DFS channels, etc.), for example, upondetecting a suspected radar event (e.g., detecting a number N of radarpulses 3302 greater than or equal to a predetermined number X of radarpulses 3302), as further described herein. In a further non-limitingaspect, exemplary embodiments of exemplary mesh network mesh nodes canbe configured to terminate or to hold its transmissions on the one ormore DFS channels upon receipt of the exemplary control, management,and/or data frame, e.g., an exemplary CTS frame 4102, etc., for example,while a suspected radar event (e.g., indicated by detection of a numberN of radar pulses 3302 greater than or equal to a predetermined number Xof radar pulses 3302) is validated or invalidated.

As a non-limiting example, FIG. 41 depicts an exemplary Wi-Ficommunications period in which exemplary mesh networks 3000, 3100, 3200,etc., comprising an exemplary mesh network node host device 3206,configured as a DFS master (e.g., a multi-channel DFS master), and a setof nearby or neighbor exemplary mesh network node host devices 3206,configured as DFS masters, as described herein, for example, regardingFIGS. 2, 13, 14, 24, 30-32, etc., and or other network devices arecommunicating wirelessly while employing one or more DFS channels. Thecommunications period depicted in FIG. 41 comprises a dirty period 4104comprising Wi-Fi traffic, followed by a clean period 4106, as a resultof terminating or holding transmissions on the one or more DFS channelsupon receipt of the exemplary control, management, and/or data frame,e.g., an exemplary CTS frame 4102, etc., for example, while thesuspected radar event (e.g., indicated by detection of a number N ofradar pulses 3302 greater than or equal to a predetermined number X ofradar pulses 3302) is validated or invalidated. Presuming that thesuspected radar event (e.g., indicated by detection of a number N ofradar pulses 3302 greater than or equal to a predetermined number X ofradar pulses 3302) is invalidated (e.g., by failing to detect a number Mof radar pulses 3302 greater than or equal to a second predeterminednumber Y of radar pulses 3302, within a predetermined timeframe, etc.),FIG. 41 further depicts resumption of Wi-Fi traffic following expirationof the clean period 4106.

For example, as further described herein, on a DFS channel, wheneverthere is an indication of a suspected radar event (e.g., detecting anumber N of radar pulses 3302 greater than or equal to a predeterminednumber X of radar pulses 3302), an exemplary DFS master does not haveinitial understanding of whether the suspected radar event (e.g.,detecting a number N of radar pulses 3302 greater than or equal to apredetermined number X of radar pulses 3302) is real, or whether it is afalse detection (e.g., by failing to detect a number M of radar pulses3302 greater than or equal to a second predetermined number Y of radarpulses 3302, within a predetermined timeframe, etc.), as a result ofvarious sources of interference (e.g., random noise 3004, adjacentchannel leakages and/or interference from other channels, etc.).Accordingly, whenever there is indication of a suspected radar event(e.g., detecting a number N of radar pulses 3302 greater than or equalto a predetermined number X of radar pulses 3302) at an exemplary DFSmaster, exemplary DFS master can be configured to transmit (e.g.,broadcast or otherwise) an exemplary control, management, and/or dataframe, e.g., a silence frame or packet, an exemplary CTS frame 4102,etc., on one or more DFS channels to facilitate silencing networktransmissions on the one or more DFS channels, for a predetermined time,e.g., two radar pulses 3302 (e.g., radar pulses 3032 of a PRI), forexample, as depicted in FIG. 41. As a result of silencing the one ormore DFS channels, any further indications of radar 3102 or subsequentindications of a suspected radar event (e.g., detecting a number M ofradar pulses 3302 greater than or equal to a second predetermined numberY of radar pulses 3302, within a predetermined timeframe, etc.), on theexemplary DFS master, another exemplary DFS master, or on any otherradar detector associated with exemplary mesh networks 3000, 3100, 3200,etc., the suspected radar event can be validated or invalidated duringthe silent or clean period 4106, without unnecessary interference on theone or more DFS channels.

In a non-limiting aspect, an exemplary control, management, and/or dataframe, e.g., an exemplary CTS frame 4102, etc., can be configured (e.g.,generated and transmitted, etc.) between two successive radar pulses3302 (e.g., generated and transmitted less than about 1 ms after initialsensing indication of the suspected radar event and of duration lessthan PRI), such that it can be received by other radar detectors in theexemplary mesh network before next expected radar pulse (e.g., based onthe initial detection of the suspected radar event and the PRI). Inanother non-limiting example, one or more DFS channels can be silencedin response to receiving exemplary control, management, and/or dataframe, e.g., an exemplary CTS frame 4102, etc., to facilitate one ormore of the other radar detectors in the exemplary mesh network havingsufficient time after receiving the exemplary control, management,and/or data frame, e.g., an exemplary CTS frame 4102, etc., to receivethe next expected, successive radar pulse 3302 (if it is to occur)without interference on the one or more DFS channels.

In the non-limiting example, regarding FIG. 41 having five successiveradar pulses 3302 (e.g., radar pulses 3302 of a PRI), exemplarynon-limiting embodiments can be configured to, upon detection of thefirst radar pulse 3302 of the five radar pulses 3302, wait to detect thesecond radar pulse 3302 of the five radar pulses 3302 (e.g., configuredto wait to detect a number N of radar pulses 3302 greater than or equalto a predetermined number X of radar pulses 3302, where N is 2 and X is2) before making a determination that there is an indication of possibleradar 3102 or a possible valid radar event. Accordingly, variousnon-limiting embodiments can ensure that any single spuriousinterference (e.g., random noise 3004, adjacent channel leakages and/orinterference from other channels, etc.) on the one or more DFS channelsis not mistaken for indication of possible radar 3102 or a possiblevalid radar event, thus reducing the probability of false detections andresultant network downtime, as further described herein. As describedabove, exemplary non-limiting embodiments can then transmit (e.g.,broadcast or otherwise) an exemplary control, management, and/or dataframe, e.g., a silence frame or packet, an exemplary CTS frame 4102,etc., on one or more DFS channels to facilitate silencing networktransmissions on the one or more DFS channels, for a predetermined time,e.g., two radar pulses 3302 (e.g., radar pulses 3032 of a PRI), forexample, as depicted in FIG. 41.

In a non-limiting aspect, exemplary control, management, and/or dataframe, e.g., a silence frame or packet, an exemplary CTS frame 4102,etc., on one or more DFS channels can be transmitted (e.g., broadcast orotherwise) immediately after the second radar pulse 3302 is detected(e.g., within a specified amount of time, such as generated and sentless than about 1 ms after initial sensing indication of the radar event(e.g., detecting a number N of radar pulses 3302 greater than or equalto a predetermined number X of radar pulses 3302, where N is 2 and X is2)). In another non-limiting aspect, exemplary control, management,and/or data frame, e.g., a silence frame or packet, an exemplary CTSframe 4102, etc., on one or more DFS channels can be transmitted (e.g.,broadcast or otherwise) immediately after the second radar pulse 3302 isdetected (e.g., within a specified amount of time, such as generated andsent less than about 120 microseconds (μs) after initial sensingindication of the radar event (e.g., detecting a number N of radarpulses 3302 greater than or equal to a predetermined number X of radarpulses 3302, where N is 2 and X is 2)). In a further non-limitingaspect, if no further pulses 3302 are detected or sensed on the one ormore DFS channels during the clean period 4106 (e.g., by failing todetect a number M of radar pulses 3302 greater than or equal to a secondpredetermined number Y of radar pulses 3302, where M is 2 and Y is 3,within a predetermined timeframe, such as clean period 4106, etc.), thenexemplary embodiments can resume transmission on the one or more DFSchannels after the clean period 4106 expires. Accordingly, variousnon-limiting embodiments can further ensure that spurious interference(e.g., random noise 3004, adjacent channel leakages and/or interferencefrom other channels, etc.) on the one or more DFS channels is notmistaken for a real radar pulse 3302, thus further reducing theprobability of false detections and resultant network downtime.

In yet another on-limiting aspect, various non-limiting embodiments canbe further configured to cease transmission on the one or more DFSchannels after the clean period 4106, for example, if further radarpulses 3302 are detected or sensed on the DFS channel during the cleanperiod 4106 (e.g., where a number N of detected radar pulses 3302greater than or equal to a predetermined number X of radar pulses 3302,where N is 2 and X is 2, but where a number M of detected radar pulses3302 is not greater than or equal to a second predetermined number Y ofradar pulses 3302, within the predetermined timeframe, such as cleanperiod 4106, etc.). In still further non-limiting aspects, exemplaryembodiments can be further configured to transmit (e.g., broadcast orotherwise) exemplary control, management, and/or data frames, e.g.,silence frames or packets, exemplary CTS frames 4102, etc., on one ormore DFS channels to facilitate silencing network transmissions on theone or more DFS channels, for a predetermined time, e.g., two radarpulses 3302 (e.g., radar pulses 3032 of a PRI), for example, as depictedin FIG. 41, simultaneously (e.g., within a specified amount of time,such as within about 2 ms, in a further non-limiting aspect),immediately after making a determination that there is an indication ofpossible radar 3102 or a possible valid radar event (e.g., after waitingto detect a number N of radar pulses 3302 greater than or equal to apredetermined number X of radar pulses 3302, where N is 2 and X is 2).As a result, various non-limiting implementations, as described herein,can facilitate determining whether signals on one or more DFS channelsare noise 3004 or actual radar 3102, while simultaneously reducing theprobability of false detections that can reduce DFS channelavailability, and/or reducing network downtime, as described herein.

In another non-limiting aspect, exemplary control, management, and/ordata frame, e.g., a silence frame or packet, an exemplary CTS frame4102, etc., transmitted on one or more DFS channels can be configured toencode the duration of the clean period 4106. In yet anothernon-limiting aspect, an exemplary CTS frame 4102 can comprise a NAVfield, as described above regarding FIG. 19A, that can be encoded byvarious disclosed embodiments the duration of the clean period 4106. Instill further non-limiting aspects, the duration of the clean period4106 encoded in the NAV field can comprise or be associated with a valueor duration equivalent of a number, C, equal to a number of pulserepetition intervals (PRIs) of the pulse 3302 of radar 3102. In yetanother non-limiting aspect, an exemplary CTS frame 4102, etc.,transmitted on one or more DFS channels can be (e.g., broadcast orotherwise) can be configured to be encoded, such that the exemplary CTSframe 4102 is specific and identifiable to a specific exemplary DFSmaster expecting to silence the one or more DFS channels. In stillfurther non-limiting aspects, other data can be supplied by variousnon-limiting embodiments to facilitate radar information propagation3008 associated with a suspected radar event (e.g., detecting a number Nof radar pulses 3302 greater than or equal to a predetermined number Xof radar pulses 3302, detecting a number M of radar pulses 3302 greaterthan or equal to a second predetermined number Y of radar pulses 3302,within a predetermined timeframe, etc.), such as, in a non-limitingexample, a predetermined and/or special destination MAC, which specifiesand indicates to devices communicating on one or more DFS channels thatthe exemplary CTS frame 4102 is specific to a radar detection purpose(e.g., from an exemplary DFS master). In still other non-limitingaspects, exemplary disclosed embodiments can be configured to transmit(e.g., broadcast or otherwise) can an exemplary CTS frame 4102, etc.,transmitted on one or more DFS channels which can comprise an exemplaryCTS-Self frame 4102. As a non-limiting example, if an exemplary radardetector suspects it detects a suspected radar event, an embodiment suchas an exemplary DFS master can be configured to transmit (e.g.,broadcast or otherwise) an exemplary CTS-Self frame 4102, which can befurther configured to encode a time field of the CTS-Self frame 4102that specifies a duration for which exemplary mesh network, mesh nodescan be directed to silence the one or more DFS channels. In a furthernon-limiting example, a time field of the CTS-Self frame 4102 can beemployed by various disclosed embodiments to identify a specifiedduration to silence the channel, a predetermined amount of time (e.g., 2radar pulses of PRI, etc.), or otherwise, not based on the predeterminedamount or a protocol specific standard duration for receiving CTSframes.

Accordingly, FIG. 41 depicts exemplary methods 4100 for reducing falsedetections and/or network downtime, based on employing a control,management, and/or data frame, in exemplary mesh networks 3000, 3100,3200, etc. employing DFS channels. For example, exemplary non-limitingembodiments (e.g., a primary DFS master, a secondary DFS master, amulti-channel DFS master, a standalone DFS master, an agility agent3208, whether embedded or distributed, etc.) of an exemplary meshnetwork (e.g., exemplary mesh network 3200, etc.) can, at 4108, detect asuspected radar event (e.g., detect a number N of radar pulses 3302greater than or equal to a predetermined number X of radar pulses 3302).At 4110, exemplary methods 4100 can further comprise transmitting anexemplary control, management, and/or data frame, e.g., an exemplary CTSframe 4102, etc. on one or more DFS channels to facilitate silencing theone or more DFS channels. As described above, exemplary non-limitingembodiments can continue to sense for a radar 3102 signal. Accordingly,at 4112, exemplary methods 4100 can further comprise further detectionof the suspected radar event (e.g., detection of a number M of radarpulses 3302 greater than or equal to a second predetermined number Y ofradar pulses 3302, within a predetermined timeframe, clean period 4106,etc.). In a further non-limiting aspect, exemplary embodiments ofexemplary mesh network mesh nodes can be configured to terminate or tohold its transmissions on the one or more DFS channels upon receipt ofthe exemplary control, management, and/or data frame, e.g., an exemplaryCTS frame 4102, etc., for example, while a suspected radar event (e.g.,indicated by detection of a number N of radar pulses 3302 greater thanor equal to a predetermined number X of radar pulses 3302) is validatedor invalidated, as further described above. Presuming that the suspectedradar event (e.g., indicated by detection of a number N of radar pulses3302 greater than or equal to a predetermined number X of radar pulses3302) is invalidated (e.g., by failing to detect a number M of radarpulses 3302 greater than or equal to a second predetermined number Y ofradar pulses 3302, within a predetermined timeframe, etc.), FIG. 41further depicts, at 4112, resumption of Wi-Fi traffic followingexpiration of the clean period 4106, and exemplary embodiments ofexemplary mesh network mesh nodes can resume exemplary methods 4100 at4108. Presuming that the suspected radar event (e.g., indicated bydetection of a number N of radar pulses 3302 greater than or equal to apredetermined number X of radar pulses 3302) is validated (e.g., byfailing to detect a number M of radar pulses 3302 greater than or equalto a second predetermined number Y of radar pulses 3302, within apredetermined timeframe, etc.), FIG. 41 further depicts, at 4114,identification of a valid radar 3102, for which, as described herein,communication on DFS channels with a radar 3102 signal is prohibited.

FIG. 42 depicts still other exemplary methods 4200 for reducing falsedetections and/or network downtime, based on employing a hold signal4202 and/or a resume signal 4204, in exemplary mesh networks 3000, 3100,3200, etc., employing DFS channels, according to various non-limitingaspects. In contrast to exemplary methods 4100, FIG. 42 depictsexemplary methods 4200 for reducing false detections and/or networkdowntime, based on employing a hold signal 4202 and/or a resume signal4204 in exemplary mesh networks 3000, 3100, 3200, etc. For example,exemplary non-limiting embodiments (e.g., a primary DFS master, asecondary DFS master, a multi-channel DFS master, a standalone DFSmaster, an agility agent 3208, whether embedded or distributed, etc.) ofan exemplary mesh network (e.g., exemplary mesh network 3200, etc.) can,at 4206, detect a suspected radar event (e.g., detect a number N ofradar pulses 3302 greater than or equal to a predetermined number X ofradar pulses 3302). At 4210, exemplary methods 4200 can further comprisetransmitting an exemplary hold signal 4202 on one or more DFS channelsto facilitate silencing the one or more DFS channels. As describedabove, an exemplary radar detector (e.g., a primary DFS master, asecondary DFS master, a multi-channel DFS master, a standalone DFSmaster, an agility agent 3208, whether embedded or distributed, etc.)that detects a suspected radar event (e.g., detect a number N of radarpulses 3302 greater than or equal to a predetermined number X of radarpulses 3302) can also be configured to be wired or wirelessly connectedto an exemplary mesh network and can be further configured to send asignal (e.g., one or more of an exemplary hold signal 4202 and/or anexemplary resume signal 4204, etc.) over the enterprise network to askone or more other routers/access points or any other mesh node in themesh network to silence, without having to send out a CTS frame (e.g.,via an out of band method, a direct ethernet frame, etc.). As a result,in a non-limiting aspect, exemplary embodiments, as described herein,can be configured to send a packet or data (e.g., one or more of anexemplary hold signal 4202 and/or an exemplary resume signal 4204, etc.)that signals nearby or neighboring devices on the same network, forwhich such nearby or neighboring devices can also be configured to turnoff their transmitter for a predetermined period of time, requestneighbor devices on the same network to assist in radar detection on theone or more DFS channels, etc. As further described above, exemplarynon-limiting embodiments can continue to sense for a radar 3102 signal.Accordingly, at 4210, exemplary methods 4200 can further comprisefurther detection of the suspected radar event (e.g., detection of anumber M of radar pulses 3302 greater than or equal to a secondpredetermined number Y of radar pulses 3302, within a predeterminedtimeframe, clean period 4106, etc.). In a further non-limiting aspect,exemplary embodiments of exemplary mesh network mesh nodes can beconfigured to terminate or to hold its transmissions on the one or moreDFS channels upon receipt of the exemplary hold signal 4202, forexample, while a suspected radar event (e.g., indicated by detection ofa number N of radar pulses 3302 greater than or equal to a predeterminednumber X of radar pulses 3302) is validated or invalidated, as furtherdescribed above. Presuming that the suspected radar event (e.g.,indicated by detection of a number N of radar pulses 3302 greater thanor equal to a predetermined number X of radar pulses 3302) isinvalidated (e.g., by failing to detect a number M of radar pulses 3302greater than or equal to a second predetermined number Y of radar pulses3302, within a predetermined timeframe, etc.), FIG. 42 further depicts,at 4210, resumption of Wi-Fi traffic following expiration of anexemplary clean period 4206, and exemplary embodiments of exemplary meshnetwork mesh nodes can resume exemplary methods 4200 at 4206. Presumingthat the suspected radar event (e.g., indicated by detection of a numberN of radar pulses 3302 greater than or equal to a predetermined number Xof radar pulses 3302) is validated (e.g., by failing to detect a numberM of radar pulses 3302 greater than or equal to a second predeterminednumber Y of radar pulses 3302, within a predetermined timeframe, etc.),FIG. 42 further depicts, at 4212, identification of a valid radar 3102,for which, as described herein, communication on DFS channels with aradar 3102 signal is prohibited.

As described above, exemplary embodiments can be configured to send apacket or data (e.g., an exemplary resume signal 4204, etc.) thatsignals nearby or neighboring devices on the same network, for whichsuch nearby or neighboring devices can also be configured to resumewireless communication on the one or more DFS channels, etc.Subsequently, as exemplary radar detector (e.g., a primary DFS master, asecondary DFS master, a multi-channel DFS master, a standalone DFSmaster, an agility agent 3208, whether embedded or distributed, etc.)determines that the suspected radar event (e.g., indicated by detectionof a number N of radar pulses 3302 greater than or equal to apredetermined number X of radar pulses 3302) is no longer present on theone or more DFS channels (e.g., by failing to detect a number M of radarpulses 3302 greater than or equal to a second predetermined number Y ofradar pulses 3302, within a predetermined timeframe, etc.), FIG. 42further depicts, at 4214, resumption of Wi-Fi traffic (e.g., uponreceipt of an exemplary resume signal 4204, following expiration of anexemplary clean period 4206, etc.), and exemplary embodiments ofexemplary mesh network mesh nodes can resume exemplary methods 4200 at4206.

In further non-limiting implementations, exemplary mesh networks cancomprise or be associated with an exemplary, standalone, dedicated DFSsensor agility agent and/or DFS master, for example, as furtherdescribed herein, regarding FIGS. 2, 13-14, 20-29, etc. In anon-limiting aspect, an exemplary, standalone, dedicated DFS sensoragility agent and/or DFS master can be configured to be directlyattached to one or more of the exemplary mesh nodes. In anothernon-limiting aspect, an exemplary, standalone, dedicated DFS sensoragility agent and/or DFS master (e.g., a multi-channel DFS master, etc.)can be configured to be part of the exemplary mesh network, for example,as further described herein, regarding exemplary mesh networks 3000,3100, 3200, etc. and/or FIGS. 2, 13, 14, 24, 30-32, etc. Accordingly,exemplary non-limiting embodiments can be configured to continuouslydetect radar on multiple DFS channels, can be configured to providewhite and black list of channels for individual mesh nodes, and/or canbe configured to provide white and black list of channels for an entireexemplary mesh network, as described herein, via an exemplary cloudintelligence engine, or otherwise. In a further non-limiting aspect, oneor more exemplary mesh nodes can be configured to receive one or more ofthe black/white channel lists from the exemplary, standalone, dedicatedDFS sensor agility agent and/or DFS master. In yet another non-limitingaspect, one or more exemplary mesh nodes can be further configured tofuse the black/white channel lists from the exemplary, standalone,dedicated DFS sensor agility agent and/or DFS master against the meshnode's local information, e.g., via an exemplary cloud intelligenceengine, or otherwise, to facilitate deriving one or more radar freechannels, and/or exemplary channel preference lists. In still anothernon-limiting aspect, one or more exemplary mesh nodes can be furtherconfigured to employ one or more exemplary channel preference lists,such that, in the event of an actual radar 3102 event, the one or moreexemplary mesh nodes and/or the entire mesh network can move to anotherDFS channel without first performing a CAC on the new channel, thusreducing the resultant network downtime such a CAC would necessitate, asfurther described herein

In still further non-limiting embodiments, one or more exemplary meshnode, one or more exemplary DFS sensor agility agent and/or DFS master,and so on, can be configured to transmit its radar events (e.g., radarinformation propagation 3008, etc.) to an exemplary cloud intelligenceengine, for example, as further described herein, regarding FIGS. 2,13-14, 20-42, etc. In a non-limiting aspect, an exemplary cloudintelligence engine, as described herein, can be further configured todata fuse the one or more radar events (e.g., radar informationpropagation 3008, etc.) against other information sources, as furtherdescribed herein. In another non-limiting aspect, an exemplary cloudintelligence engine, as described herein, can be further configured toemploy inference and/or algorithms to discriminate between random noise3004 and radar 3102 to facilitate reducing false detections and/ornetwork downtime in exemplary mesh networks employing DFS channels, asdescribed herein, and can be further configured to make one or moredeterminations regarding the probability of the one or more radar events(e.g., radar information propagation 3008, etc.) being an actual radar3102. In yet another non-limiting aspect, an exemplary cloudintelligence engine, as described herein, can be further configured totransmit one or more recommendations regarding the one or more radarevents, e.g., to one or more exemplary mesh nodes, to one or moreexemplary mesh network groups, and so on, regarding the one or moreradar event, regarding channel switching, regarding updates to one ormore of blacklists, whitelists, preference lists, etc., as furtherdescribed herein.

As described above, in a further non-limiting aspect, one or moreexemplary mesh networks, and/or portions thereof, can be operated in oneor more groups of exemplary mesh nodes, for example, based in part onone or more grouping criteria. In a non-limiting aspect, it can bedesirable to group certain exemplary mesh nodes of an exemplary meshnetwork together, e.g., from a network performance perspective, from alocation perspective, from a quality of service perspective, and so on,without limitation. Accordingly, exemplary embodiments, as describedherein, can employ one or more of blacklists, whitelists, and/or channelpreference lists, that can be different in at least one aspect betweendifferent groups of exemplary mesh nodes, that can be similar orsubstantially similar between exemplary mesh nodes within an exemplarygroup, based in part on one or more grouping criteria. In still furthernon-limiting aspects, the one or more grouping criteria can comprise orbe associated with network performance, location information, and/ordevice characteristics associated with one or more exemplary mesh nodes,quality of service, regulatory information, spectral information, and soon, without limitation. Accordingly, in a non-limiting aspect, one ormore of the blacklists, whitelists, and/or channel preference lists canbe shared within an exemplary mesh group. Thus, in a furthernon-limiting aspect, exemplary mesh nodes, such as, for example, anexemplary access point, an exemplary router, an exemplary agility agent,an exemplary mesh network node host device 3206, configured as a DFSmaster (e.g., a multi-channel DFS master), and so on, can be configuredto segregate exemplary mesh nodes into two or more groups, can beconfigured group two or more exemplary mesh nodes within an exemplarygroup, based in part on one or more grouping criteria, as describedherein, e.g., via an exemplary cloud intelligence engine, or otherwise.

Accordingly, exemplary mesh nodes, such as, for example, an exemplaryaccess point, an exemplary router, an exemplary agility agent, anexemplary mesh network node host device 3206, configured as a DFS master(e.g., a multi-channel DFS master), and so on, can be configured as aradar sensor, which can be further configured to provide an exemplarycloud intelligence engine with DFS channel radar information (e.g.,radar information propagation 3008), location information, spectralinformation, etc., as further described herein, regarding FIGS. 2,13-14, 20-42, etc. Thus, in a further non-limiting aspect, an exemplarycloud intelligence engine can be configured to data fuse suchinformation and against external sources, as described herein, and canbe further configured to provide one or more of blacklists, whitelists,and/or channel preference lists (e.g., one or more of group blacklists,group whitelists, group channel preference lists, etc.) to the exemplarymesh network, based in part on one or more grouping criteria. As aresult, exemplary mesh nodes, such as, for example, an exemplary accesspoint, an exemplary router, an exemplary mesh network node host device3206, configured as a DFS master (e.g., a multi-channel DFS master),etc., can be configured to employ one or more group blacklists, groupwhitelists, group channel preference lists, etc., for two or more groupsin communication with the exemplary mesh node, as described herein,e.g., via an exemplary cloud intelligence engine, or otherwise. Asdescribed herein, an exemplary channel preference list can comprise alist of DFS channels, where if one DFS channel is required to bevacated, every exemplary mesh node in the exemplary mesh network havingthe exemplary channel preference lists can be configured to switch tothe next DFS channel in the exemplary channel preference list, whichfacilitates an exemplary mesh network of exemplary mesh nodes as singlemesh network group configured to share the singular exemplary groupchannel preference list. Further non-limiting embodiments, can beconfigured to employ similar sharing of exemplary blacklists andexemplary whitelists among a singular exemplary group of exemplary meshnodes as single mesh network group and/or can be configured to employtwo or more groups of the blacklists, whitelists, and/or channelpreference lists (e.g., one or more of group blacklists, groupwhitelists, group channel preference lists, etc.), as described herein.

FIG. 43 depicts further exemplary methods 4300 for reducing falsedetections and/or network downtime in exemplary mesh networks employingDFS channels, according to various non-limiting aspects. Accordingly, at4302, FIG. 43 depicts exemplary methods 4300 comprising receiving in amesh network (e.g., exemplary mesh network 3200, etc.), at a host devicecomprising a processor (e.g., exemplary mesh network node host device3206, configured as a DFS master, such as a multi-channel DFS master,etc.), an indication of a suspected radar event on one or more dynamicfrequency selection (DFS) channel, as further described herein, forexample, regarding FIGS. 2, 13, 14, 24, 30-42, etc.

In addition, at 4304, exemplary methods 4300 for reducing falsedetections and/or network downtime can further comprise determining,with the host device (e.g., exemplary mesh network node host device3206, configured as a DFS master, such as a multi-channel DFS master,etc.), whether the suspected radar event is a valid radar event, basedat least in part on the suspected radar event, as further describedherein, for example, regarding FIGS. 2, 13, 14, 24, 30-42, etc. As anon-limiting example, exemplary methods 4300 can further comprisedetermining whether the suspected radar event is the valid radar eventbased in part on additional radar information (e.g., from another,nearby, or neighbor mesh node, or host device (e.g., exemplary meshnetwork node host device 3206, configured as a DFS master, such as amulti-channel DFS master, etc.), etc.).

In further non-limiting embodiments, at 4306, exemplary methods 4300 canfurther comprise propagating, in the mesh network (e.g., exemplary meshnetwork 3200, etc.), radar information (e.g., radar informationpropagation 3008, etc.) regarding one or more of the suspected radarevent or the valid radar event to one or more of another host device(e.g., exemplary mesh network node host device 3206, configured as a DFSmaster, such as a multi-channel DFS master, etc.) or a cloudintelligence engine (e.g., cloud intelligence engine 235, 2435, etc.)associated with the mesh network (e.g., exemplary mesh network 3200,etc.), as further described herein, for example, regarding FIGS. 2, 13,14, 24, 30-42, etc. In a non-limiting aspect, exemplary methods 4300 cancomprise propagating location information associated with the hostdevice (e.g., exemplary mesh network node host device 3206, configuredas a DFS master, such as a multi-channel DFS master, etc.). Innon-limiting aspects, exemplary methods 4300 can further comprisepropagating next channel information that identifies to the one or moreof another host device (e.g., exemplary mesh network node host device3206, configured as a DFS master, such as a multi-channel DFS master,etc.) or the cloud intelligence engine (e.g., cloud intelligence engine235, 2435, etc.) associated with the mesh network (e.g., exemplary meshnetwork 3200, etc.) a next channel of the one or more DFS channels totransfer communications based in part on one or more of the suspectedradar event or the valid radar event, and/or encoding the next channelinformation in one or more beacon signal of the host device (e.g.,exemplary mesh network node host device 3206, configured as a DFSmaster, such as a multi-channel DFS master, etc.), as further describedherein, for example, regarding FIGS. 2, 13, 14, 24, 30-42, etc.

At 4308 of FIG. 43, exemplary methods 4300 can further comprisereceiving additional radar information (e.g., radar informationpropagation 3008, etc.), including, but not limited to, locationinformation, from one or more of another host device (e.g., exemplarymesh network node host device 3206, configured as a DFS master, such asa multi-channel DFS master, etc.) or the cloud intelligence engine(e.g., cloud intelligence engine 235, 2435, etc.) associated with themesh network (e.g., exemplary mesh network 3200, etc.).

In addition, FIG. 43, depicts exemplary methods 4300 further comprisingdetermining that the suspected radar event is a valid radar event or aninvalid radar event, at 4310, and propagating one or more of theadditional radar information (e.g., radar information propagation 3008,etc.), the valid radar event, or the additional location information tothe one or more of another host device (e.g., exemplary mesh networknode host device 3206, configured as a DFS master, such as amulti-channel DFS master, etc.) or the cloud intelligence engine (e.g.,cloud intelligence engine 235, 2435, etc.) associated with the meshnetwork (e.g., exemplary mesh network 3200, etc.), at 4312, as furtherdescribed herein, for example, regarding FIGS. 2, 13, 14, 24, 30-42,etc. As a non-limiting example, exemplary methods 4300 can comprisedetermining that the suspected radar event is a valid or an invalidradar event, such as, for example, by validating the radar event withtwo or more radar sensors or detectors (e.g., a primary DFS master, asecondary DFS master, a multi-channel DFS master, a standalone DFSmaster, an agility agent, whether embedded or distributed, etc.)associated with the host device (e.g., exemplary mesh network node hostdevice 3206, configured as a DFS master, such as a multi-channel DFSmaster, etc.), and/or based in part on one or more of the additionalradar information (e.g., radar information propagation 3008, etc.), theadditional location information, or other information received from theone or more of another host device (e.g., exemplary mesh network nodehost device 3206, configured as a DFS master, such as a multi-channelDFS master, etc.) or the cloud intelligence engine (e.g., cloudintelligence engine 235, 2435, etc.) associated with the mesh network(e.g., exemplary mesh network 3200, etc.).

In still further non-limiting implementations, exemplary methods 4300can further comprise transmitting, in the mesh network (e.g., exemplarymesh network 3200, etc.), one or more of a CTS signal 4102 or a holdsignal 4202 on the one or more DFS channel based in part on thereceiving the indication of the suspected radar event, encodinginformation in the one or more of the CTS signal 4102 or the hold signal4202, wherein the information is associated with a predetermined time orduration during which communication on the one or more DFS channel is tobe silenced, and/or transmitting, in the mesh network (e.g., exemplarymesh network 3200, etc.), a resume signal 4204, for which thecommunication on the one or more DFS channel is to be resumed, asfurther described herein, for example, regarding FIGS. 2, 13, 14, 24,30-42, etc. In addition, exemplary methods for 300 can further compriseupdating, with the host device (e.g., exemplary mesh network node hostdevice 3206, configured as a DFS master, such as a multi-channel DFSmaster, etc.), one or more of a channel blacklist, a channel whitelist,or a channel preference list based at least in part on one or more ofthe suspected radar event or the valid radar event, and/or updating theone or more of the channel blacklist, the channel whitelist, or thechannel preference list for a group comprising at least the host device(e.g., exemplary mesh network node host device 3206, configured as a DFSmaster, such as a multi-channel DFS master, etc.) and the another hostdevice (e.g., exemplary mesh network node host device 3206, configuredas a DFS master, such as a multi-channel DFS master, etc.), based atleast in part on one or more grouping criterion for the group, whereinthe group comprises less than all mesh nodes in the mesh network (e.g.,exemplary mesh network 3200, etc.) communicating on the one or more DFSchannels, as further described herein, for example, regarding FIGS. 2,13, 14, 24, 30-42, etc.

FIG. 44 depicts a functional block diagram illustrating examplenon-limiting devices or systems suitable for use with aspects of thedisclosed subject matter. For instance, FIG. 44 illustrates examplenon-limiting devices or systems 4400 suitable for performing variousaspects of the disclosed subject matter in accordance with an exemplaryDFS master 3206, 3208, etc. (e.g., a primary DFS master, a secondary DFSmaster, a multi-channel DFS master, a standalone DFS master, an agilityagent, whether embedded or distributed, etc.) detecting a suspectedradar event, as further described herein. In non-limiting embodiments,exemplary devices or systems 4400 can comprise one or more radar sensorsor detectors (e.g., a primary DFS master, a secondary DFS master, amulti-channel DFS master, a standalone DFS master, an agility agent,whether embedded or distributed, etc.) associated with the host device(e.g., exemplary mesh network node host device 3206, configured as a DFSmaster, such as a multi-channel DFS master, etc.) configured to receivean indication of a suspected radar event on one or more DFS channel in amesh network (e.g., exemplary mesh network 3200, etc.), as furtherdescribed herein, for example, regarding FIGS. 2, 13, 14, 24, 30-42,etc.

In further non-limiting embodiments, example devices or systems 4400 cancomprise one or more processors of a multi-channel DFS master device(e.g., exemplary mesh network node host device 3206, configured as a DFSmaster, such as a multi-channel DFS master, etc.) configured todetermine whether the suspected radar event is a valid radar event,based at least in part on the indication of the suspected radar event,as further described herein, for example, regarding FIGS. 2, 13, 14, 24,30-42, etc. In a non-limiting example, exemplary limitations of devicesor systems 4400 can comprise the multi-channel DFS master device (e.g.,exemplary mesh network node host device 3206, configured as a DFSmaster, such as a multi-channel DFS master, etc.) further configured todetermine that the suspected radar event is a valid or an invalid radarevent, for example, as further described herein, via one or more radardetectors, such as an exemplary agility agent 3208, whether embedded, ordistributed, associated with the multi-channel DFS master device (e.g.,exemplary mesh network node host device 3206, configured as a DFSmaster, such as a multi-channel DFS master, etc.), and whether via anexemplary cloud intelligence engine (e.g., cloud intelligence engine235, 2435, etc.), or otherwise.

In other non-limiting implementations of example devices or systems4400, disclose embodiments can comprise exemplary multi-channel DFSmaster device (e.g., exemplary mesh network node host device 3206,configured as a DFS master, such as a multi-channel DFS master, etc.)further configured to update one or more of a channel blacklist, achannel whitelist, or a channel preference list based in part on one ormore of the suspected radar event or the valid radar event, as furtherdescribed herein, for example, regarding FIGS. 2, 13, 14, 24, 30-42,etc. in another non-limiting aspect, the exemplary multi-channel DFSmaster device (e.g., exemplary mesh network node host device 3206,configured as a DFS master, such as a multi-channel DFS master, etc.)can be further configured to update the one or more of the channelblacklist, the channel whitelist, or the channel preference list for agroup comprising at least the multi-channel DFS master device and themesh node, based at least in part on one or more grouping criterion forthe group, wherein the group comprises less than all mesh nodes in themesh network (e.g., exemplary mesh network 3200, etc.) communicating onthe one or more DFS channels.

In addition, example devices or systems 4400 can further comprise one ormore communications components 4402 associated the multi-channel DFSmaster device configured to propagate, in the mesh network, radarinformation regarding one or more of the suspected radar event or thevalid radar event to one or more of a mesh node (e.g., an exemplary meshnode) or a cloud intelligence engine (e.g., cloud intelligence engine235, 2435, etc.) associated with the mesh network. In a non-limitingaspect, exemplary communications component 4402 can comprise or beassociated with one or more of agility agent 200, DFS master 2400, hostdevice 3002, exemplary mesh network node host device 3206, configured asa DFS master, such as a multi-channel DFS master, etc., and so on, orportions thereof. In a non-limiting aspect, the one or morecommunications components 4402 can be further configured to propagatelocation information associated with the multi-channel DFS master device(e.g., exemplary mesh network node host device 3206, configured as a DFSmaster, such as a multi-channel DFS master, etc.). In anothernon-limiting aspect, the one or more communications components 4402 canbe further configured to receive and/or propagate additional radarinformation (e.g., radar information propagation 3008) from the one ormore of the mesh node (e.g., exemplary mesh node) or the cloudintelligence engine (e.g., cloud intelligence engine 235, 2435, etc.)associated with the mesh network (e.g., exemplary mesh network 3200,etc.), and can be configured to propagate one or more of the additionalradar information (e.g., radar information propagation 3008, etc.), avalid or an invalid radar event, or the additional location informationto the one or more of mesh node (e.g., exemplary mesh node) or the cloudintelligence engine (e.g., cloud intelligence engine 235, 2435, etc.)associated with the mesh network (e.g., exemplary mesh network 3200,etc.), and so on.

In still further non-limiting aspects, the one or more communicationscomponents 4402 can be further configured to transmit, in the meshnetwork (e.g., exemplary mesh network 3200, etc.), one or more of a CTSsignal 4102 or a hold signal 4202 on the one or more DFS channels basedat least in part on the indication of the suspected radar event, and canbe configured to encode information in the one or more of the CTS signal4102 or the hold signal 4204, wherein the information is associated witha predetermined time or duration during which communication on the oneor more DFS channels is to be silenced, as further described herein, forexample, regarding FIGS. 2, 13, 14, 24, 30-42, etc. In addition, the oneor more communications components 4402 can be further configured totransmit, in the mesh network (e.g., exemplary mesh network 3200, etc.)a resume signal 4204, for which the communication on the one or more DFSchannels is to be resumed, as further described herein.

In still other non-limiting aspects, the one or more communicationscomponents 4402 can be further configured to propagate next channelinformation that identifies to the one or more of mesh node (e.g.,exemplary mesh node) or the cloud intelligence engine (e.g., cloudintelligence engine 235, 2435, etc.) associated with the mesh network(e.g., exemplary mesh network 3200, etc.) a next channel of the one ormore DFS channels to transfer communications based in part on one ormore of the suspected radar event or the valid radar event, and/or canbe configured to encode the next channel information in one or morebeacon signal of the multi-channel DFS master device (e.g., exemplarymesh network node host device 3206, configured as a DFS master, such asa multi-channel DFS master, etc.), as further described herein, forexample, regarding FIGS. 2, 13, 14, 24, 30-42, etc.

FIG. 45 depicts an example non-limiting device or system 4500, orportions thereof, suitable for performing various aspects of thedisclosed subject matter. The device or system 4500, or portionsthereof, can be a stand-alone device or a portion thereof, a speciallyprogrammed computing device or a portion thereof (e.g., a memoryretaining instructions for performing the techniques as described hereincoupled to a processor), and/or a composite device or system comprisingone or more cooperating components distributed among several devices, asfurther described herein. As an example, example non-limiting device orsystem 4500, or portions thereof, can comprise example devices and/orsystems regarding FIGS. 2, 24, etc., as described above, or portionsthereof, for example, regarding agility agent 200, DFS master 2400, amulti-channel DFS master device (e.g., exemplary mesh network node hostdevice 3206, configured as a DFS master, such as a multi-channel DFSmaster, etc.), and so on. For example, FIG. 45 depicts an example device4500, such as an exemplary mesh node, as further described herein, forexample, regarding FIGS. 2, 13, 14, 24, 30-42, etc.

Accordingly, device or system 4500 can comprise a memory 4502 thatretains various instructions with respect to facilitating variousoperations, for example, such as: receiving in a mesh network (e.g.,mesh network 3200, etc.) an indication of a suspected radar event on oneor more DFS channels; determining whether the suspected radar event is avalid radar event, based in part on the suspected radar event;propagating, in the mesh network (e.g., mesh network 3200, etc.), radarinformation (e.g., radar information propagation 3008, etc.) regardingone or more of the suspected radar event or the valid radar event to oneor more of one or more mesh (e.g., exemplary mesh node) or a cloudintelligence engine (e.g., cloud intelligence engine 235, 2435, etc.)associated with the mesh network (e.g., exemplary mesh network 3200,etc.); means for transmitting, in the mesh network (e.g., exemplary meshnetwork 3200, etc.), one or more of a CTS signal 4102 or a hold signal4202 on the one or more DFS channel based in part on the receiving theindication of the suspected radar event; means for transmitting, in themesh network (e.g.,), a resume signal 4202, for which the communicationon the at least one DFS channel is to be resumed; and/or the like.

Additionally, memory 4502 can retain further and/or alternativeinstructions for performing various functions and/or operationsdescribed herein including instructions associated with performingexemplary methods described herein, for example, as further describedherein, for example, regarding FIGS. 2, 13, 14, 24, 30-43, etc. Thus,the above example instructions and other suitable instructions forfunctionalities and/or operations as described herein for example,regarding FIGS. 2, 13, 14, 24, 30-43, etc., can be retained withinmemory 4502, such as memory 202, 2402, 249, 2449, and a processor 4504,such as processor 203, 2403, 250, 2450, etc. can be utilized inconnection with executing the instructions, without limitation.

Example Networked and Distributed Environments

One of ordinary skill in the art can appreciate that the variousembodiments of the disclosed subject matter and related systems,devices, and/or methods described herein can be implemented inconnection with any computer or other client or server device, which canbe deployed as part of a communications system, a computer network,and/or in a distributed computing environment, and can be connected toany kind of data store. In this regard, the various embodimentsdescribed herein can be implemented in any computer system orenvironment having any number of memory or storage units, and any numberof applications and processes occurring across any number of storageunits or volumes, which may be used in connection with communicationsystems using the techniques, systems, and methods in accordance withthe disclosed subject matter. The disclosed subject matter can apply toan environment with server computers and client computers deployed in anetwork environment or a distributed computing environment, havingremote or local storage. The disclosed subject matter can also beapplied to standalone computing devices, having programming languagefunctionality, interpretation and execution capabilities for generating,receiving, storing, and/or transmitting information in connection withremote or local services and processes.

Distributed computing provides sharing of computer resources andservices by communicative exchange among computing devices and systems.These resources and services can include the exchange of information,cache storage and disk storage for objects, such as files. Theseresources and services can also include the sharing of processing poweracross multiple processing units for load balancing, expansion ofresources, specialization of processing, and the like. Distributedcomputing takes advantage of network connectivity, allowing clients toleverage their collective power to benefit the entire enterprise. Inthis regard, a variety of devices can have applications, objects orresources that may utilize disclosed and related systems, devices,and/or methods as described for various embodiments of the subjectdisclosure.

FIG. 46 provides a schematic diagram of an example networked ordistributed computing environment. The distributed computing environmentcomprises computing objects 4610, 4612, etc. and computing objects ordevices 4620, 4622, 4624, 4626, 4628, etc., which may include programs,methods, data stores, programmable logic, etc., as represented byapplications 4630, 4632, 4634, 4636, 4638. It can be understood thatobjects 4610, 4612, etc. and computing objects or devices 4620, 4622,4624, 4626, 4628, etc. may comprise different devices, such as PDAs,audio/video devices, mobile phones, MP3 players, personal computers,laptops, etc.

Each object 4610, 4612, etc. and computing objects or devices 4620,4622, 4624, 4626, 4628, etc. can communicate with one or more otherobjects 4610, 4612, etc. and computing objects or devices 4620, 4622,4624, 4626, 4628, etc. by way of the communications network 4640, eitherdirectly or indirectly. Even though illustrated as a single element inFIG. 46, network 4640 may comprise other computing objects and computingdevices that provide services to the system of FIG. 46, and/or mayrepresent multiple interconnected networks, which are not shown. Eachobject 4610, 4612, etc. or 4620, 4622, 4624, 4626, 4628, etc. can alsocontain an application, such as applications 4630, 4632, 4634, 4636,4638, that can make use of an API, or other object, software, firmwareand/or hardware, suitable for communication with or implementation ofdisclosed and related systems, devices, methods, and/or functionalityprovided in accordance with various embodiments of the subjectdisclosure. Thus, although the physical environment depicted may showthe connected devices as computers, such illustration is merely exampleand the physical environment may alternatively be depicted or describedcomprising various digital devices, any of which can employ a variety ofwired and/or wireless services, software objects such as interfaces, COMobjects, and the like.

There are a variety of systems, components, and network configurationsthat support distributed computing environments. For example, computingsystems can be connected together by wired or wireless systems, by localnetworks or widely distributed networks. Currently, many networks arecoupled to the Internet, which can provide an infrastructure for widelydistributed computing and can encompass many different networks, thoughany network infrastructure can be used for example communications madeincident to employing disclosed and related systems, devices, and/ormethods as described in various embodiments.

Thus, a host of network topologies and network infrastructures, such asclient/server, peer-to-peer, or hybrid architectures, can be utilized.The “client” is a member of a class or group that uses the services ofanother class or group to which it is not related. A client can be aprocess, e.g., roughly a set of instructions or tasks, that requests aservice provided by another program or process. The client processutilizes the requested service without having to “know” any workingdetails about the other program or the service itself.

In a client/server architecture, particularly a networked system, aclient is usually a computer that accesses shared network resourcesprovided by another computer, e.g., a server. In the illustration ofFIG. 46, as a non-limiting example, computers 4620, 4622, 4624, 4626,4628, etc. can be thought of as clients and computers 4610, 4612, etc.can be thought of as servers where servers 4610, 4612, etc. provide dataservices, such as receiving data from client computers 4620, 4622, 4624,4626, 4628, etc., storing of data, processing of data, transmitting datato client computers 4620, 4622, 4624, 4626, 4628, etc., although anycomputer can be considered a client, a server, or both, depending on thecircumstances. Any of these computing devices may be processing data,forming metadata, synchronizing data or requesting services or tasksthat may implicate disclosed and related systems, devices, and/ormethods as described herein for one or more embodiments.

A server is typically a remote computer system accessible over a remoteor local network, such as the Internet or wireless networkinfrastructures. The client process can be active in a first computersystem, and the server process can be active in a second computersystem, communicating with one another over a communications medium,thus providing distributed functionality and allowing multiple clientsto take advantage of the information-gathering capabilities of theserver. Any software objects utilized pursuant to disclosed and relatedsystems, devices, and/or methods can be provided standalone, ordistributed across multiple computing devices or objects.

In a network environment in which the communications network/bus 4640 isthe Internet, for example, the servers 4610, 4612, etc. can be Webservers with which the clients 4620, 4622, 4624, 4626, 4628, etc.communicate via any of a number of known protocols, such as thehypertext transfer protocol (HTTP). Servers 4610, 4612, etc. may alsoserve as clients 4620, 4622, 4624, 4626, 4628, etc., as may becharacteristic of a distributed computing environment.

Example Computing Device

As mentioned, advantageously, the techniques described herein can beapplied to devices or systems where it is desirable to employ disclosedand related systems, devices, and/or methods. It should be understood,therefore, that handheld, portable and other computing devices andcomputing objects of all kinds are contemplated for use in connectionwith the various disclosed embodiments. Accordingly, the below generalpurpose remote computer described below in FIG. 47 is but one example ofa computing device. Additionally, disclosed and related systems,devices, and/or methods can include one or more aspects of the belowgeneral purpose computer, such as display, storage, analysis, control,etc.

Although not required, embodiments can partly be implemented via anoperating system, for use by a developer of services for a device orobject, and/or included within application software that operates toperform one or more functional aspects of the various embodimentsdescribed herein. Software can be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by one or more computers, such as client workstations, serversor other devices. Those skilled in the art will appreciate that computersystems have a variety of configurations and protocols that can be usedto communicate data, and thus, no particular configuration or protocolshould be considered limiting.

FIG. 47 thus illustrates an example of a suitable computing systemenvironment 4700 in which one or aspects of the embodiments describedherein can be implemented, although as made clear above, the computingsystem environment 4700 is only one example of a suitable computingenvironment and is not intended to suggest any limitation as to scope ofuse or functionality. Neither should the computing environment 4700 beinterpreted as having any dependency or requirement relating to any oneor combination of components illustrated in the example operatingenvironment 4700.

With reference to FIG. 47, an example remote device for implementing oneor more embodiments includes a general purpose computing device in theform of a computer 4710. Components of computer 4710 can include, butare not limited to, a processing unit 4720, a system memory 4730, and asystem bus 4722 that couples various system components including thesystem memory to the processing unit 4720.

Computer 4710 typically includes a variety of computer readable mediaand can be any available media that can be accessed by computer 4710.The system memory 4730 can include computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) and/orrandom access memory (RAM). By way of example, and not limitation,memory 4730 can also include an operating system, application programs,other program modules, and program data.

A user can enter commands and information into the computer 4710 throughinput devices 4740. A monitor or other type of display device is alsoconnected to the system bus 4722 via an interface, such as outputinterface 4750. In addition to a monitor, computers can also includeother peripheral output devices such as speakers and a printer, whichcan be connected through output interface 4750.

The computer 4710 can operate in a networked or distributed environmentusing logical connections to one or more other remote computers, such asremote computer 4770. The remote computer 4770 can be a personalcomputer, a server, a router, a network PC, a peer device or othercommon network node, or any other remote media consumption ortransmission device, and can include any or all of the elementsdescribed above relative to the computer 4710. The logical connectionsdepicted in FIG. 47 include a network 4772, such local area network(LAN) or a wide area network (WAN), but can also include othernetworks/buses. Such networking environments are commonplace in homes,offices, enterprise-wide computer networks, intranets and the Internet.

As mentioned above, while example embodiments have been described inconnection with various computing devices and network architectures, theunderlying concepts can be applied to any network system and anycomputing device or system in which it is

Also, there are multiple ways to implement the same or similarfunctionality, e.g., an appropriate API, tool kit, driver code,operating system, control, standalone or downloadable software object,etc. which enables applications and services to use disclosed andrelated systems, devices, methods, and/or functionality. Thus,embodiments herein are contemplated from the standpoint of an API (orother software object), as well as from a software or hardware objectthat implements one or more aspects of disclosed and related systems,devices, and/or methods as described herein. Thus, various embodimentsdescribed herein can have aspects that are wholly in hardware, partly inhardware and partly in software, as well as in software.

Example Mobile Device

FIG. 48 depicts a schematic diagram of an example mobile device 4800(e.g., a mobile handset) that can facilitate various non-limitingaspects of the disclosed subject matter in accordance with theembodiments described herein. Although mobile handset 4800 isillustrated herein, it will be understood that other devices can be amobile device, as described herein, for instance, and that the mobilehandset 4800 is merely illustrated to provide context for theembodiments of the subject matter described herein. The followingdiscussion is intended to provide a brief, general description of anexample of a suitable environment 4800 in which the various embodimentscan be implemented. While the description includes a general context ofcomputer-executable instructions embodied on a tangible computerreadable storage medium, those skilled in the art will recognize thatthe subject matter also can be implemented in combination with otherprogram modules and/or as a combination of hardware and software.

Generally, applications (e.g., program modules) can include routines,programs, components, data structures, etc., that perform particulartasks or implement particular abstract data types. Moreover, thoseskilled in the art will appreciate that the methods described herein canbe practiced with other system configurations, includingsingle-processor or multiprocessor systems, minicomputers, mainframecomputers, as well as personal computers, hand-held computing devices,microprocessor-based or programmable consumer electronics, and the like,each of which can be operatively coupled to one or more associateddevices.

A computing device can typically include a variety of computer readablemedia. Computer readable media can comprise any available media that canbe accessed by the computer and includes both volatile and non-volatilemedia, removable and non-removable media. By way of example and notlimitation, computer readable media can comprise tangible computerreadable storage and/or communication media. Tangible computer readablestorage can include volatile and/or non-volatile media, removable and/ornon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, or other data. Tangible computer readable storage caninclude, but is not limited to, RAM, ROM, EEPROM, flash memory or othermemory technology, CD ROM, digital video disk (DVD) or other opticaldisk storage, magnetic cassettes, magnetic tape, magnetic disk storageor other magnetic storage devices, or any other medium which can be usedto store the desired information and which can be accessed by thecomputer.

Communication media, as contrasted with tangible computer readablestorage, typically embodies computer readable instructions, datastructures, program modules, or other data in a modulated data signalsuch as a carrier wave or other transport mechanism, and includes anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of the anyof the above should also be included within the scope of computerreadable communications media as distinguishable from computer-readablestorage media.

The handset 4800 can include a processor 4802 for controlling andprocessing all onboard operations and functions. A memory 4804interfaces to the processor 4802 for storage of data and one or moreapplications 4806 (e.g., communications applications such as browsers,apps, etc.). Other applications can support operation of communicationsand/or financial communications protocols. The applications 4806 can bestored in the memory 4804 and/or in a firmware 4808, and executed by theprocessor 4802 from either or both the memory 4804 or/and the firmware4808. The firmware 4808 can also store startup code for execution ininitializing the handset 4800. A communications component 4810interfaces to the processor 4802 to facilitate wired/wirelesscommunication with external systems, e.g., cellular networks, VoIPnetworks, and so on. Here, the communications component 4810 can alsoinclude a suitable cellular transceiver 4811 (e.g., a GSM transceiver)and/or an unlicensed transceiver 4813 (e.g., Wireless Fidelity (WiFi™),Worldwide Interoperability for Microwave Access (WiMax®)) forcorresponding signal communications. The handset 4800 can be a devicesuch as a cellular telephone, a PDA with mobile communicationscapabilities, and messaging-centric devices. The communicationscomponent 4810 also facilitates communications reception fromterrestrial radio networks (e.g., broadcast), digital satellite radionetworks, and Internet-based radio services networks.

The handset 4800 includes a display 4812 for displaying text, images,video, telephony functions (e.g., a Caller ID function), setupfunctions, and for user input. For example, the display 4812 can also bereferred to as a “screen” that can accommodate the presentation ofmultimedia content (e.g., music metadata, messages, wallpaper, graphics,etc.). The display 4812 can also display videos and can facilitate thegeneration, editing and sharing of video quotes. A serial I/O interface4814 is provided in communication with the processor 4802 to facilitatewired and/or wireless serial communications (e.g., Universal Serial Bus(USB), and/or Institute of Electrical and Electronics Engineers (IEEE)4894) through a hardwire connection, and other serial input devices(e.g., a keyboard, keypad, and mouse). This supports updating andtroubleshooting the handset 4800, for example. Audio capabilities areprovided with an audio I/O component 4816, which can include a speakerfor the output of audio signals related to, for example, indication thatthe user pressed the proper key or key combination to initiate the userfeedback signal. The audio I/O component 4816 also facilitates the inputof audio signals through a microphone to record data and/or telephonyvoice data, and for inputting voice signals for telephone conversations.

The handset 4800 can include a slot interface 4818 for accommodating aSIC (Subscriber Identity Component) in the form factor of a cardSubscriber Identity Module (SIM) or universal SIM 4820, and interfacingthe SIM card 4820 with the processor 4802. However, it is to beappreciated that the SIM card 4820 can be manufactured into the handset4800, and updated by downloading data and software.

The handset 4800 can process Internet Protocol (IP) data traffic throughthe communication component 4810 to accommodate IP traffic from an IPnetwork such as, for example, the Internet, a corporate intranet, a homenetwork, a person area network, etc., through an ISP or broadband cableprovider. Thus, VoIP traffic can be utilized by the handset 4800 andIP-based multimedia content can be received in either an encoded or adecoded format.

A video processing component 4822 (e.g., a camera and/or associatedhardware, software, etc.) can be provided for decoding encodedmultimedia content. The video processing component 4822 can aid infacilitating the generation and/or sharing of video. The handset 4800also includes a power source 4824 in the form of batteries and/or analternating current (AC) power subsystem, which power source 4824 caninterface to an external power system or charging equipment (not shown)by a power input/output (I/O) component 4826.

The handset 4800 can also include a video component 4830 for processingvideo content received and, for recording and transmitting videocontent. For example, the video component 4830 can facilitate thegeneration, editing and sharing of video. A location-tracking component4832 facilitates geographically locating the handset 4800. A user inputcomponent 4834 facilitates the user inputting data and/or makingselections as previously described. The user input component 4834 canalso facilitate selecting perspective recipients for fund transfer,entering amounts requested to be transferred, indicating accountrestrictions and/or limitations, as well as composing messages and otheruser input tasks as required by the context. The user input component4834 can include such conventional input device technologies such as akeypad, keyboard, mouse, stylus pen, and/or touch screen, for example.

Referring again to the applications 4806, a hysteresis component 4836facilitates the analysis and processing of hysteresis data, which isutilized to determine when to associate with an access point. A softwaretrigger component 4838 can be provided that facilitates triggering ofthe hysteresis component 4838 when a WiFi™ transceiver 4813 detects thebeacon of the access point. A SIP client 4840 enables the handset 4800to support SIP protocols and register the subscriber with the SIPregistrar server. The applications 4806 can also include acommunications application or client 4846 that, among otherpossibilities, can be target for transfer money plugin or user interfacecomponent functionality as described above.

The handset 4800, as indicated above related to the communicationscomponent 4810, includes an indoor network radio transceiver 4813 (e.g.,WiFi™ transceiver). This function supports the indoor radio link, suchas IEEE 802.11, for the dual-mode Global System for MobileCommunications (GSM) handset 4800. The handset 4800 can accommodate atleast satellite radio services through a handset that can combinewireless voice and digital radio chipsets into a single handheld device.

In the present specification, the term “or” is intended to mean aninclusive “or” rather than an exclusive “or.” That is, unless specifiedotherwise, or clear from context, “X employs A or B” is intended to meanany of the natural inclusive permutations. That is, if X employs A; Xemploys B; or X employs both A and B, then “X employs A or B” issatisfied under any of the foregoing instances. Moreover, articles “a”and “an” as used in this specification and annexed drawings shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from context to be directed to a singular form.

In addition, the terms “example” and “such as” are utilized herein tomean serving as an instance or illustration. Any embodiment or designdescribed herein as an “example” or referred to in connection with a“such as” clause is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. Rather, use of the terms“example” or “such as” is intended to present concepts in a concretefashion. The terms “first,” “second,” “third,” and so forth, as used inthe claims and description, unless otherwise clear by context, is forclarity only and does not necessarily indicate or imply any order intime.

What has been described above includes examples of one or moreembodiments of the disclosure. It is, of course, not possible todescribe every conceivable combination of components or methodologiesfor purposes of describing these examples, and it can be recognized thatmany further combinations and permutations of the present embodimentsare possible. Accordingly, the embodiments disclosed and/or claimedherein are intended to embrace all such alterations, modifications andvariations that fall within the spirit and scope of the detaileddescription and the appended claims. Furthermore, to the extent that theterm “includes” is used in either the detailed description or theclaims, such term is intended to be inclusive in a manner similar to theterm “comprising” as “comprising” is interpreted when employed as atransitional word in a claim.

What is claimed is:
 1. A method comprising: receiving in a mesh network,at a host device comprising a processor, an indication of a suspectedradar event on at least one dynamic frequency selection (DFS) channel;determining, with the host device, whether the suspected radar event isa valid radar event, based at least in part on the suspected radarevent; and propagating, in the mesh network, radar information regardingat least one of the suspected radar event or the valid radar event to atleast one of another host device or a cloud intelligence engineassociated with the mesh network.